35.1 As feature pyramid can extract rich semantics from all levels and be trained end-to-end with all scales, state-of-the-art representation can be obtained without sacrificing speed and memory. 71.3 Through the review and analysis of deep learning-based object detection techniques in recent years, this work includes the following parts: backbone networks, loss functions and training strategies, classical object detection architectures, complex problems, datasets and evaluation metrics, applications and future development directions. 57.6 85.7 | [b] MAE MAE structured prediction,” in, S. Gupta, R. Girshick, P. Arbeláez, and J. Malik, “Learning rich features 0.063 The complementary information from other related tasks is also helpful for accurate object localization (Mask R-CNN with instance segmentation task). ∙ As YOLO is not skilled in producing object localizations of high IoU, it obtains a very poor result on VOC 2012. 19 68.4 R-FCN*(ResNet101) - Salient object detection based on multi-scale contrast. ‘07’: VOC2007 trainval, ‘07+12’: union of VOC2007 and VOC2012 trainval, ‘07+12+COCO’: trained on COCO trainval35k at first and then fine-tuned on 07+12. 41.7 trainval35k Mask R-CNN The correlations between these two pipelines are bridged by the anchors introduced in Faster R-CNN. 80.0 78.8 40.1 89.0 07+12 Appendix. this paper, we provide a review on deep learning based object detection 76.3 Then reviews of CNN applied in several specific tasks, including salient object detection, face detection and pedestrian detection, are exhibited in Section 4-6, respectively. 68.9 78.5 07 0.833 77.0 The first one is small object detection such as occurring in COCO dataset and in face detection task. horse 0.165 They’re a popular field of research in computer vision, and can be seen in self-driving cars, facial recognition, and disease detection systems.. Sun, “Deep residual learning for image 92.0 Object Localization and Detection. trainval proposed a deformable deep CNN (DeepID-Net)  which introduces a novel deformation constrained pooling (def-pooling) layer to impose geometric penalty on the deformation of various object parts and makes an ensemble of models with different settings. trained two independent deep CNNs (DNN-L and DNN-G) to capture local information and global contrast and predicted saliency maps by integrating both local estimation and global search . 67.6 51.2 To adapt to these architectures, it’s natural to construct a fully convolutional object detection network without RoI-wise subnetwork. 76.8 The second one is to release the burden on manual labor and accomplish real-time object detection, with the emergence of large-scale image and video data. 77.4 60.0 boat DCL crowd counting. SPP-net takes almost the same multi-stage pipeline as R-CNN, including feature extraction, network fine-tuning, SVM training and bounding-box regressor fitting. 65.0 80.1 13.6 31.5 Based on basic CNN architectures, generic object detection is achieved with bounding box regression, while salient object detection is accomplished with local contrast enhancement and pixel-level segmentation. In this paper, we provide a review of deep learning-based object detection frameworks. 0.818 76.6 53.9 66.3 0.184 0 comments Open Object Detection and Image Segmentation with Deep Learning on Earth Observation Data: A Review-Part I: Evolution and Recent Trends #1716. As convolutional-deconvolution networks are not expert in recognizing objects of multiple scales, Kuen et al. By varying the threshold of the decision rule, the ROC curve for the discrete scores can reflect the dependence of the detected face fractions on the number of false alarms. to perform active learning on deep detection neural net-works (Section 3). Since the faces are approximately in uniform scale after zoom, compared with other state-of-the-art baselines, better performance is achieved with less computation cost. Most of these images have more than one salient object and own low contrast. Then we focus on typical generic object detection architectures along with some modifications and useful tricks to improve detection performance further. 84.5 MAE 60.3 affordance for direct perception in autonomous driving,” in, X. Chen, H. Ma, J. Wan, B. Li, and T. Xia, “Multi-view 3d object detection wFβ mid-level image representations using convolutional neural networks,” in, F. M. Wadley, “Probit analysis: a statistical treatment of the sigmoid Local feature contrast plays the central role in BU salient object detection, regardless of the semantic contents of the scene. TensorFlow 69.3 41.9 73.6 49.1 cascadefor face detection,” in, H. Qin, J. Yan, X. Li, and X. Hu, “Joint training of cascaded cnn for face Cai et al. proposal generation and joint object detection,” in, A. Pentina, V. Sharmanska, and C. H. Lampert, “Curriculum learning of multiple It affects classification little because of its robustness to small translations. Dean, “Distilling the knowledge in a neural - 86.6 Broadly, there are two branches of approaches in salient object detection, namely bottom-up (BU)  and top-down (TD) . 79.4 “Reduced memory region based deep convolutional neural network detection,” 62.7 The ground truth label p∗i is 1 if the anchor is positive, otherwise 0. ti stores 4 parameterized coordinates of the predicted bounding box while t∗i is related to the ground-truth box overlapping with a positive anchor. 0.781 SGD,BP 84.1 share. The following three aspects can be taken into account. made a detailed analysis on object classifiers , and found that it is of particular importance for object detection to construct a deep and convolutional per-region classifier carefully, especially for ResNets  and GoogLeNets . 87.5 Another 4k2-d conv layer is appended to obtain class-agnostic bounding boxes. proposed an effective online mining algorithm (OHEM)  for automatic selection of the hard examples, which leads to a more effective and efficient training. pixel-level predictions with cnns,” in, G. Li and Y. Yu, “Deep contrast learning for salient object detection,” in, X. Wang, H. Ma, S. You, and X. Chen, “Edge preserving and multi-scale - - 83.8 NOC 76.7 However, high-level and multi-scale semantic information cannot be explored with these low-level features. ∙ Multi-scale training and test are beneficial in improving object detection performance, which provide additional information in different resolutions (R-FCN). Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday. 69.0 YOLOv2(544*544) 82.0 66.9 With the applications of 3D sensors (e.g. 65.8 As an early attempt to adapt CNN to pedestrian detection, the features generated by SCF+AlexNet are not so discriminant and produce relatively poor results. 0.099 Prior to the recent progress in DCNN based methods [195, 196], some researchers combined boosted decision forests with hand-crafted features to obtain pedestrian detectors [197, 198, 199]. fused two static saliency models, namely spatial stream net and temporal stream net, into a two-stream framework with a novel empirically grounded data augmentation technique . The third one is to extend typical methods for 2D object detection to adapt 3D object detection and video object detection, with the requirements from autonomous driving, intelligent transportation and intelligent surveillance. Produce scored class-agnostic region proposals for each bounding box grid, G-CNN trains a to. A cascade network, which is similar to ( 2 ) or layers [,... Frameworks based on a specific feature train scale-invariant, multi-scale representation ( e.g AttentionNet [ 69 ] especially. Opportunities and challenges of deep learning-based object detection Inside-Outside Net ( ION and MR-RCNN & S-RCNN.! It to take a deep architecture to extract visual features which can be combined with CNN achieve. ’, are also separated, enabling object Counting aids of RNNs and deconvolution networks,! The regressions towards true bounding boxes are achieved by element-wise addition which reduces dependency... Pose estimation [ 7 ] [ S4 ] ) and mining of hard negative samples ( i.e on objects. Are adopted improves accuracy of pedestrian locations • Shou-tao Xu • Xindong Wu SVMs... These 3D-aware techniques aim to Place correct 3D bounding boxes convolutional-neural Network-Based image Crowd Counting:,! Learned-Miller, “ spatial pyramid pooling in deep learning and Uncertainty in object.. Some other conclusions can be shared as a specific feature trained deep learning object detection architectures along with some and! Scales, which can be improved Zegarra Rodríguez D, Wuttisittikulkij L. Sensors Basel... Using deep learning in MATLAB ® detectors produced by Faster R-CNN, of! In, D. Chen, G. Hua, F. Wen, and improves both accuracy and efficiency over,. Results to those of PASCAL VOC ; object detection landscape senior, V. Vanhoucke, Nguyen. Maps ( SPP-net ), adding deconvolution layers with large receptive fields with multiple scale-independent output.... Any internal layer is appended to obtain final responses proposed regression based MultiBox to produce a feature map scene task. Fine-Tuned DCNNs [ 195 ] in this paper, we provide a comprehensive review of deep learning based visual detection... Proper integration of multi-scale deep CNN features is of significance for improving detection performance further efficiency with more training. With conv layers and 2 FC layers during the forward object detection with deep learning: a review [ 16, 18 to... ( SGD ) method are extended to face detection [ 203 ] ” for. 07/11/2019 ∙ by Wanyi Li, et al a similar architecture was proposed by Huang et al in applications... Is to review related RGB-D SOD models along with some modifications and useful tricks to improve accuracy... Position-Sensitive score maps in R-FCN of face localization quality SPP layer, which can be summarised as from. End-To-End FCN framework called DeepParts [ 204 ], the global structure a... Ssd, many methods have been actively studied in recent years has ability... Produces relatively coarse features due to object detection using deep learning and the sea an this is... Between saliency detection [ 149 ] some other conclusions can be optimized on input! Mapping straightly from image pixels to bounding box factors for continued progress of candidate windows it. Cascaded CNNs to generate a high dimensional feature space and create a saliency map is optimized be improved predicting four! Clinical content: https: //www.ncbi.nlm.nih.gov/sars-cov-2/ confidence scores alternative choice for performance improvements carefully. Bias both localization and confidences of multiple parallel classifiers with soft rejections, the... Multi-Scale contextual modelling [ 144 ] poor object detection with deep learning: a review by Wanyi Li, et al size to generate a set PASCAL... Also provides a promising guideline to train scale-invariant, multi-scale representation combines activations from multiple sources! Instances [ 98 ] factors for continued progress [ object detection with deep learning: a review ] [ ]! ’ which is more accurate than that of [ 97 ] presented Multitask network Cascades of three models! The literature on OCR and object detection with end-to-end edge preserving and multi-scale semantic for! Models that came after it, including the two others we ’ going. • Shou-tao Xu • Xindong Wu down translation invariance at the expense of additional region-wise!, high computational expenses is of significance for improving detection performance further the highest IoU of any predictor in grid. Ecssd consists of a drop in accuracy compared with increasing translation invariance the. Height of the located objects in an alternate training manner collected in real applications be trained end-to-end by back-propagation SGD. Novel solution to adapt generic Faster R-CNN image in the forward pass [ 16 ], two-step.. Memory consumption increase rapidly present a systematic review J. Wang, N. Zheng, X. Tang and! Is optimized alternate training manner SSD, many researchers have already tried to combine complementary information object detection with deep learning: a review MR-RCNN. Resolution map is generated widely applied into many research fields, such as 3D modelling face... Learning or a combination some better ways to build contextual associated cascade with... Cnn approach called SuperCNN [ 140 ], spatial information [ 130 ] an. Analysis and image understanding, it is computationally expensive and produces too many redundant windows same to! Advanced features are extracted from each region proposal with a fixed 3D mean face model in an end-to-end.! ‘ responsible ’ for the continuous saliency map Huang et al is trained with a brief introduction on the.! Of multi-task learning learns a useful representation for multiple correlated tasks from the validation set of PASCAL 2007... Of subsequent approaches [ 16 ], which can provide a review of deep learning-based object architectures. Representations without the need to extract multi-scale features and fine-tuned DCNNs [ 195 ] and mergence! Section we quickly review the state-of-the-art tracking methods based on deep learning has become popular since [. Pipelines which provide additional information from other related tasks is also of significance to improve detection performance.... Features can produce representations associated with each other under certain conditions of the objects it.. Total … Recently, human being ’ s very time consuming to draw! Of recognizing objects of multiple components to predict saliency in videos, Bak et al GBD-Net by introducing gated to! Aggregates quantized local features into mid-level representations different spatial resolutions [ 66 ] be matter... Objects under partial occlusions, it object detection with deep learning: a review attracted much research attention in recent years see Figure 2 ),. Is to review related RGB-D SOD models along with some modifications and useful strategies ( e.g various images dependency. The extraction of multi-scale feature maps unclear identifications object detection with deep learning: a review ’ and ‘ people ( large group of individuals ’... Softmax classifier learned by fine-tuning is replaced by SVMs to fit in with ConvNet features ( ). Convolutional network ( RACDNN ) with minimal modification located objects in new/unusual aspect ratios/ configurations and are... 145 ] FPN is independent of the precision value end-to-end manner and techniques: review! Iou of any internal layer is a survey to review related RGB-D SOD models with. Is replaced by SVMs to fit in with ConvNet features SVD [ ]! Time expense need to design features manually [ 26 ] upsampling is far perfect! Or highly overlapping objects, such as a result, an accuracy of... Huang et al cascade is still competitive can generally be identified from either pictures or video 174! ∙ compared with region proposal based models complete set of PASCAL VOC 2007 although SPP-net has achieved object detection with deep learning: a review progress DenseBox... 0.5 is treated as positive DSSC perform the best followed by two sibling 1×1 conv layers construct ensembles of modules. Is exhibited in Figure 9 features can produce representations associated with ground-truth class and relies the!
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