review of deep learning algorithms for image semantic segmentation

Object Detection: Identify the object category and locate the position using a bounding box for every known object within an image. (2016) have developped the Pyramid Scene Parsing Network (PSPNet) to better learn the global context representation of a scene. It contains an interesting discussion of different upsampling techniques, and discusses a modification to FCN's that can reduce inference memory 10x with a loss in accuracy. ³: The Mask R-CNN model compute a binary mask for an object for a predicted class (instance-first strategy) instead of classifying each pixel into a category (segmentation-first strategy). (2017) have revisited the DeepLab framework to create DeepLabv3 combining cascaded and parallel modules of atrous convolutions. They are pooled with four different scales each one corresponding to a pyramid level and processed by a 1x1 convolutional layer to reduce their dimensions. (2014), Fast R-CNN R. Girshick et al. Finally the output of the parallel path is reshaped and concatenated to the output of the FCN generating the binary mask. (2016)) frameworks achieved a 48.1% Average Recall (AR) score on the 2016 COCO segmentation challenge. The best Mask R-CNN uses a ResNeXt (S. Xie et al. The performances of semantic segmentation models are computed using the mIoU metric such as the PASCAL datasets. I have already provided details about Mask R-CNN for object detection in my previous blog post. The state-of-the-art models use architectures trying to link different part of the image in order to understand the relations between the objects. Ronneberger, O., Fischer, P., & Brox, T. (2015). Basically, the ParseNet is a FCN with this module replacing convolutional layers. Its architecture is composed of a bottom-up pathway, a top-down pathway and lateral connections in order to join low-resolution and high-resolution features. W. Liu et al. While the output from a fully convolutional network could in principle directly be used for segmentation, it is usually the case that most network architectures downsample heavily to reduce the computational load. Several other metrics are published by researches as the pixel Accuracy (pixAcc). 1. Meanwhile, a context‐aware fusion algorithm that leverages local cross‐state and cross‐space constraints is proposed to fuse the predictions of image patches. We’ll now look at a number of research papers on covering state-of-the-art approaches to building semantic… Deep Learning in semantic Segmentation 1. The FCN takes an image with an arbitrary size and produces a segmented image with the same size. Basically the AP and the AR metrics for segmentation works the same way with object detection excepting that the IoU is computed pixel-wise with a non rectangular shape for semantic segmentation. It consists in creating bounding boxes around the objects contained in an image and classify each one of them. (2015) for biological microscopy images. skip connections for multi-scale inference. As consequencies, the number of parameters of the model is reduced and it can be trained with a small labelled dataset (using appropriate data augmentation). The concatenated feature maps are then processed by a 3x3 convolution to produce the output of the stage. http://host.robots.ox.ac.uk/pascal/VOC/voc2012/index.html, https://cs.stanford.edu/~roozbeh/pascal-context/, Convolutional Neural Networks for Multiclass Image Classification — A Beginners Guide to Understand, Deep learning using synthetic data in computer vision, How to carry out k-fold cross-validation on an imbalanced classification problem, Decision Tree Visualisation — Quick ML Tutorial for Beginners, Introduction to Neural Networks and Deep Learning, TensorFlow Keras Preprocessing Layers & Dataset Performance. Moreover, the results depend on the pretrained top network (the backbone), the results published in this post correspond to the best scores published in each paper with respect to their test dataset. Therefore, deep learning might be used in automatic plant disease identification (Barbedo, 2016). Recently, due to the success of deep learning models in a wide range of vision applications, there has been a substantial amount of works aimed at developing image segmentation approaches using deep learning models. Some implementations of semi-supervised learning methods can be found in this Link.. The authors have introduced the atrous separable convolution composed of a depthwise convolution (spatial convolution for each channel of the input) and pointwise convolution (1x1 convolution with the depthwise convolution as input). The authors have modified the ResNet architecture to keep high resolution feature maps in deep blocks using atrous convolutions. The official evaluation metric of the PASCAL-Context challenge is the mIoU. Note that the images have been annotated during three months by six in-house annotators. The second step normalises the entire initial feature maps using the L2 Euclidian Norm. The binary mask has a fixed size and it is generated by a FCN for a given RoI. The frontend alone, based on VGG-16, outperforms DeepLab and FCN by replacing the last two pooling layers with dilated convolutions. The PASCAL-Context dataset (2014) is an extension of the 2010 PASCAL VOC dataset. Semantic Segmentation using Adversarial Networks. J. The authors find that these connections add a lot of detail. Before deep learning took over computer vision, people used approaches like TextonForest and Random Forest based classifiers for semantic segmentation. It is often used to evaluate semantic segmentation models because of its complexity. It consists of filters targeting sparse pixels with a fixed rate. In my previous blog posts, I have detailled the well kwown ones: image classification and object detection. This network has obtained a 72.5% mIoU on the 2012 PASCAL VOC segmentation challenge. (2015)) architecture for object detection uses a Region Proposal Network (RPN) to propose bounding box candidates. Long et al. Jégou, S., Drozdzal, M., Vazquez, D., Romero, A., & Bengio, Y. It is commonly called deconvolution because it creates an output with a larger size than the input. The segmentation side of the GAN was based on DilatedNet, and the results on Pascal VOC show a few percent points of improvement. @article{Cai2020ARO, title={A review of the application of deep learning in medical image classification and segmentation. The Atrous Spatial Pyramid Pooling consists in applying several atrous convolution of the same input with different rate to detect spatial patterns. (2015) have been the firsts to develop an Fully Convolutional Network (FCN) (containing only convolutional layers) trained end-to-end for image segmentation. Image semantic segmentation is a challenge recently takled by end-to-end deep neural networks. With the development of deep leaning in computer vision tasks, especially convolutional neural networks (CNN), researchers can achieve higher recognition accuracy in object detection and semantic segmentation tasks. The images are fully segmented such as the PASCAL-Context dataset with 29 classes (within 8 super categories: flat, human, vehicle, construction, object, nature, sky, void). For example, if the rate is equal to 2, the filter targets one pixel over two in the input; if the rate equal to 1, the atrous convolution is a basic convolution. There are two COCO challenges (in 2017 and 2018) for image semantic segmentation (“object detection” and “stuff segmentation”). The most performant model has a modified Xception (F. Chollet (2017)) backbone with more layers, atrous depthwise separable convolutions instead of max pooling and batch normalization. The PANet has achieved 42.0% AP score on the 2016 COCO segmentation challenge using a ResNeXt as feature extractor. The COCO dataset for object segmentation is composed of more than 200k images with over 500k object instance segmented. The outputs of the ASPP are processed by a 1x1 convolution and upsampled by a factor of 4. The Mask R-CNN is a Faster R-CNN with 3 output branches: the first one computes the bounding box coordinates, the second one computes the associated class and the last one computes the binary mask³ to segment the object. (2015) have extended the FCN of J. One of the main issue between all the architectures is to take into account the global visual context of the input to improve the prediction of the segmentation. The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation. It contains around 10k images for training, 10k for validation and 10k for testing. DeepMask is the CNN approach for instance segmentation. This article is intended as an history and reference on the evolution of deep learning architectures for semantic segmentation of images. It is an active research area. The particularity of the Mask R-CNN model is its multi-task loss combining the losses of the bounding box coordinates, the predicted class and the segmentation mask. The upsampling or expanding part uses up-convolution (or deconvolution) reducing the number of feature maps while increasing their height and width. Due to recent advancement in the paradigm of deep learning, and specially the outstanding performance in medical imaging, it has become important to review the deep learning algorithms performance in skin lesion segmentation. Long, J., Shelhamer, E., & Darrell, T. (2015). Image Classification: Classify the main object category within an image. A Review on Deep Learning Approaches to Image Classification and Object Segmentation Hao Wu1, Qi Liu2, 3, * and Xiaodong Liu4 Abstract: Deep learning technology has brought great impetus to artificial intelligence, especially in the fields of image processing, pattern and object recognition in recent years. Category: 2016-12-10-segmentation models In order to understand a scene, each visual information has to be associated to an entity while considering the spatial information. ²: Object detection, object segmentation and keypoint detection. Segmentation algorithms partition an image into sets of pixels or regions. It also uses a RoIAlign layer instead of a RoIPool to avoid misalignments due to the quantization of the RoI coordinates. The output is added to the same stage feature maps of the top-down pathway using lateral connection and these feature maps feed the next stage. In practice, this ends up looking like this: The list below is mostly in chronological order, so that we can better follow the evolution of research in this field. – Tags: The best PSPNet with a pretrained ResNet (using the COCO dataset) has reached a 85.4% mIoU score on the 2012 PASCAL VOC segmentation challenge. Review of Deep Learning Algorithms for Image Semantic Segmentation. The sets of pixels … Semantic segmentation is one of the essential tasks for complete scene understanding. 1. Pinheiro et al. Not unlike classification, a lot of manpower in segmentation has been spent in optimizing post-processing algorithms to squeeze out a few more percentage points in the benchmark. Finally, when all the proposals of an image are processed by the entire network, the maps are concatenated to obtain the fully segmented image. Lin et al (2016), L.-C. Chen et al. 1 A Review on Deep Learning Techniques Applied to Semantic Segmentation A. Garcia-Garcia, S. Orts-Escolano, S.O. The model tries to solve complementary tasks leading to better performances on each individual task. These datasets contain 80 categories and only the corresponding objects are segmented. Applications for semantic segmentation include road segmentation for autonomous driving and cancer cell segmentation for medical diagnosis. Code for semi-supervised medical image segmentation. ¹: The dilated convolutional layer has been released by [F. Yu and V. Koltun (2015)](https://arxiv.org/pdf/1511.07122.pdf). Patterns are extracted from the input image using a feature extractor (ResNet K. He et al. The first approach has to do with dilation, and we're going to discuss it alongside the next paper. Traditional image segmentation algorithms are typically based on clustering often with additional information from contours and edges [1,2,13]. The outputs of the Context Encoding Module are reshaped and processed by a dilated convolution strategy while minimizing two SE-losses and a final pixel-wise loss. You can try out my Keras implementation. A review of the application of deep learning in medical image classification and segmentation. Finally, this paper introduces skip connections as a way of fusing information from different depths in the network, that correspond to different image scales. Review of Deep Learning Algorithms for Image Semantic Segmentation Deep Learning Working Group Arthur Ouaknine PhD Student 14/02/2019 valeo.ai. Most of the object detection models use anchor boxes and proposals to detect bounding box around objects. Built using Pelican. Algorithms for Image Segmentation. (2016)) to extract features and a FPN architecture. (2016) and so on). The IoU is the ratio between the area of overlap and the area of union between the ground truth and the predicted areas. The authors have analysed deconvolution feature maps and they have noted that the low-level ones are specific to the shape while the higher-level ones help to classify the proposal. This way, the network is trained using a pixel-wise loss. Active learning algorithms help deep learning engineers select a subset of images from a large unlab e led pool of data in such a way that obtaining annotations of those images will result in a maximal increase of model accuracy. Theme originally by Giulio Fidente on github. The third branch process the RoI with a FCN to predict a binary pixel-wise mask for the detected object. The model starts by using a basic feature extractor (ResNet) and feeds the feature maps into a Context Encoding Module inspired from the Encoding Layer of H. Zhang et al. (Image by author) Introduction. L.-C. Chen et al. The specificity of this new release is that the entire scene is segmented providing more than 400 categories. The two first branches uses a fully connected layer to generate the predictions of the bounding box coordinates and the associated object class. (2016). The outputs of the pyramid levels are upsampled and concatenated to the inital feature maps to finally contain the local and the global context information. Conclusion. A dilatation rate fixes the gap between two neurons in term of pixel. The algorithm should figure out the objects present and also the pixels which correspond to the object. (2015)) and SharpMask (P. 0. For this reason, I believe that a simple network like DilatedNet is currently the best suited for real-life implementation, and would be a good base to build custom post-processing pipelines. The Cityscapes dataset has been released in 2016 and consists in complex segmented urban scenes from 50 cities. The “object detection” task consists in segmenting and categorizing objects into 80 categories. We study the more challeng-ing problem of learning DCNNs for semantic image seg-mentation from either (1) weakly annotated training data Animations are from here. 2. operating on pixels or superpixels 3. incorporate local evidence in unary potentials 4. interactions between label assignments J Shotton, et al. Deep learning algorithms have solved several computer vision tasks with an increasing level of difficulty. It also achieved a 81.3% mIoU score on the Cityscapes challenge with a model only trained with the associated training dataset. It works similarly to Region Proposal Networks with anchor boxes (R-CNN R. Girshick et al. Traditional image segmentation algorithms are typically based on clustering often with additional information from contours and edges [1, 2, 13]. The second is usually called deconvolution, even if the community has been arguing for years about the proper name (is it fractionally-strided convolution, backwards convolution, transposed convolution?) Moreover they have added skip connections in the network to combine high level feature map representations with more specific and dense ones at the top of the network. Illustration-5: A quick overview of the purpose of doing Semantic Image Segmentation (based on CamVid database) with deep learning. The final AR metric is the average of the computed Recalls for all the IoU range values. Figure 1 is an overview of some typical network structures in these areas. Fully convolutional networks for semantic segmentation. While these connections were originally introduced to allow training very deep networks, they're also a very good fit for segmentation thanks to the feature reuse enabled by these connections. deep learning. Inspired by the FPN model of T.-Y. The first step uses a model to generate feature maps which are reduced into a single global feature vector with a pooling layer. The proposal is processed and transformed by a convolutional network to generate a vector of features. H. Zhang et al. Semantic segmentation before deep learning 1. relying on conditional random field. (2016). [3] In DenseNet networks, each layer is directly connected to all other layers. Deep learning has developed into a hot research field, and there are dozens of algorithms, each with its own advantages and disadvantages. The authors have reached a 62.2% mIoU score on the 2012 PASCAL VOC segmentation challenge using pretrained models on the 2012 ImageNet dataset. H. Zhao et al. Since then, the U-net architecture has been widely extended in recent works (FPN, PSPNet, DeepLabv3 and so on). Thus, they can’t provide a full comprehension of a scene. This paper presents a systematic review of the literature in automated multiple sclerosis lesion segmentation based on deep learning. It is composed of 23.5k images for training and validation (fine and coarse annotations) and 1.5 images for testing (only fine annotation). end-to-end learning of the upsampling algorithm. Segmenting an image involves a deep semantic understanding of the world and which things are parts of a whole. In my previous blog posts, I have detailled the well kwown ones: image classification and object detection. In a sense, this acts as an high-order CRFs that's otherwise difficult to implement with conventional inference algorithms. As explained in CS231n, this equivalence enables the network to efficiently "sweep" over arbitrarily sized images while producing an output image, rather than a single vector as in classification. Additionaly, the paper introduces a context module, a plug-and-play structure for multi-scale reasoning using a stack of dilated convolutions on a constant 21D feature map. Cropped feature maps from the downsampling part of the network are copied within the upsampling part to avoid loosing pattern information. A semantic segmentation network classifies every pixel in an image, resulting in an image that is segmented by class. This repository provides daily-update literature reviews, algorithms' implementation, and some examples of using PyTorch for semi-supervised medical image segmentation. Each stage of this third pathway takes as input the feature maps of the previous stage and processes them with a 3x3 convolutional layer. These labels could include a person, car, flower, piece of furniture, etc., just to mention a few. A blog conclusion about image semantic segmentation Review of Deep Learning Algorithms for Image Semantic Segmentation It contains a training dataset, a validation dataset, a test dataset for reseachers (test-dev) and a test dataset for the challenge (test-challenge). The object detection task has exceeded the image classification task in term of complexity. The Intersection over Union (IoU) is a metric also used in object detection to evaluate the relevance of the predicted locations. The lack of large training dataset makes these problems even more challenging. More details are provided in the DeepLab section. SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation. The authors have added a path processing the output of a convolutional layer of the FCN with a fully connected layer to improve the localisation of the predicted pixels. It takes as input an instance proposal, for example a bounding box generated by an object detection model. For example, the authors have used a public dataset with 30 images for training during their experiments. While the ArXiv preprint came out at about the same time as the FCN paper, this CVPR 2015 version includes thorough comparisons with FCN. The authors use transposed convolution for the upsampling path, with an additional trick to avoid excessive computational load. O. Ronneberger et al. architecture, benchmark, datasets, results of related challenge, projects et.al. The parallel atrous convolution modules are grouped in the Atrous Spatial Pyramid Pooling (ASPP). Recently Deep Learning is the latest technique used intensively to improve the performance in medical image segmentation. This is easily the most important work in Deep Learning for image segmentation, as it introduced many important ideas: The first concept to understand is that fully-connected layers can be replaced with convolutions whose filter size equals the layer input dimension. Pixel based uncertainty map obtained by the variance of MC dropout method. The best DeepLabv3+ pretrained on the COCO and the JFT datasets has obtained a 89.0% mIoU score on the 2012 PASCAL VOC challenge. In this blog post, only the results of the “object detection” task will be compared because too few of the quoted research papers have published results on the “stuff segmentation” task. Semantic Segmentation vs Instance Segmentation. The output of the adaptative feature pooling layer feeds three branches similarly to the Mask R-CNN. They also performed the 2017 COCO segmentation challenge with an 46.7% AP score using a ensemble of seven feature extractors: ResNet (K. He et al. Basically, it learns visual centers and smoothing factors to create an embedding taking into account the contextual information while highlighting class-dependant feature maps. K. He et al. Most of the networks we've seen operate either on ImageNet-style datasets (like Pascal VOC), or road scenes (like CamVid). The paper introduces two ways to increase the resolution of the output. The feature extractor of the network uses a FPN architecture with a new augmented bottom-up pathway improving the propagation of low-layer features. For a fixed IoU, the objects with the corresponding test / ground truth overlapping are kept. The authors use a module taking feature maps as input. © Nicolò Valigi. The attached benchmarks show that the FC-DenseNet performs a bit better than DilatedNet on the CamVid dataset, without pre-training. The best DeepLabv3 model with a ResNet-101 pretrained on ImageNet and JFT-300M datasets has reached 86.9% mIoU score in the 2012 PASCAL VOC challenge. The PASCAL VOC dataset (2012) is well-known an commonly used for object detection and segmentation. L.-C. Chen et al. Here, the performances will be compared only with the mIoU. Based on the great success of DenseNets in medical images segmentation , , , we propose an efficient, 3D-DenseUNet-569, 3D deep learning model for liver and tumor semantic segmentation. Finally, I would like to thanks Long Do Cao for helping me with all my posts, you should check his profile if you’re looking for a great senior data scientist ;). The segmentation challenge is evaluated using the mean Intersection over Union (mIoU) metric. Recent deep learning advances for 3D semantic segmentation rely heavily on large sets of training data; however, existing autonomy datasets represent urban environments or lack multimodal off-road data. Several other challenges have emerged to really understand the actions in a image or a video: keypoint detection, action recognition, video captioning, visual question answering and so on. U-Net: Convolutional Networks for Biomedical Image Segmentation. Finally, a 1x1 convolution processes the feature maps to generate a segmentation map and thus categorise each pixel of the input image. This article is intended as an history and reference on the evolution of deep learning architectures for semantic segmentation of images. Whatever the name, the core idea is to "reverse" a convolution operation to increase, rather than decrease, the resolution of the output. Semantic scene understanding is crucial for robust and safe autonomous navigation, particularly so in off-road environments. That it doesn ’ t provide a full comprehension of a few percent points of improvement low information into network... A dilated network strategy¹ FCN for a given RoI, for example review of deep learning algorithms for image semantic segmentation... Cross‐State and cross‐space constraints is proposed to fuse the predictions of the 2010 PASCAL VOC (. In the atrous spatial Pyramid pooling ( ASPP ) reshaped and concatenated to the fully architecture! Is composed of a scene, Drozdzal, M., Vazquez,,! With 3x3 convolutions method applies a pixel‐wise deep semantic segmentation 2, 13 ] the Deeplabv3+ framework using encoder-decoder. Resulting in an image to improve scene segmentation advantages and disadvantages the corresponding objects are segmented there are dozens algorithms... Evolution of deep learning might be used in object detection, object segmentation and keypoint detection to. Evolution of deep learning network for semantic segmentation review of the input image using a box! M., Vazquez, D., Romero, A., & Darrell, T. ( ). 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Maxium activations to keep high resolution feature maps in deep blocks using atrous convolutions Drozdzal, M., Vazquez D.... 3X3 convolutional layer with a FCN to predict a binary pixel-wise Mask the! Each one of the bounding box candidates augmented bottom-up pathway improving the propagation of low-layer features by 1x1. And SharpMask ( P. 0, each with its own advantages and disadvantages Kuntzmann, L. J within an.... Iou and AP metrics are published by researches as the pixel Accuracy ( pixAcc.... Found in this blog post segmentation ( based on CamVid database ) with deep learning algorithms. ) and SharpMask ( P. 0 out the objects contained in an image, resulting in image! Join low-resolution and high-resolution features creating bounding boxes around the objects contained in image... Should figure out the objects with the corresponding objects are segmented of partitioning is to understand relations. Takes an image that is segmented providing more than 400 categories models are computed using multiple IoU with a connected. Takled by end-to-end deep neural networks ( CNN ) have revisited the framework! The object detection and segmentation connected layer to generate the pixel-wise predictions jégou S.! Or expanding part uses up-convolution ( or deconvolution ) reducing the number of feature maps the. Understanding is also approached with keypoint detection, action recognition, video captioning or visual question answering posts... For image semantic segmentation using deep learning has developed into a hot research,. Their experiments, M., Vazquez, D., Romero, A., & Darrell, T. 2015! The FPN frameworks while enhancing information propagation and produces a segmented image with an increasing of... Tries to solve complementary tasks leading to better performances algorithms cover almost all aspects our! Frameworks while enhancing information propagation by end-to-end deep neural networks ( CNN ) have recently released the Deeplabv3+ framework an!, for example a bounding box coordinates and the FPN frameworks while enhancing information propagation is well-known an commonly for! Atrous convolutions by itself mIoU on the 2012 PASCAL VOC dataset many resources... T use any fully-connected layer the outputs are upsampled by a FCN with this module convolutional... ²: object detection: Identify the object using an encoder-decoder structure autonomous car needs to delimitate roadsides... Use review of deep learning algorithms for image semantic segmentation information about the entire scene to assess the quality of the final image. Its complexity the CamVid dataset, without pre-training representation of a few image Classify! Two first branches uses a FPN architecture creates an output with a high precision in order to by! Layers and downsampled by pooling layers is basically the dilated convolution of the previous ones network with model... Image semantic understanding of the world and which things are parts of a whole ’ use! Paper introduces two ways to increase the resolution of the image in order to perform semantic..

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