Open AccessPosted Content
You Only Look Once: Unified, Real-Time Object Detection
TLDR
YOLO as discussed by the authors predicts bounding boxes and class probabilities directly from full images in one evaluation, which can be optimized end-to-end directly on detection performance, and achieves state-of-the-art performance.Abstract:
We present YOLO, a new approach to object detection. Prior work on object detection repurposes classifiers to perform detection. Instead, we frame object detection as a regression problem to spatially separated bounding boxes and associated class probabilities. A single neural network predicts bounding boxes and class probabilities directly from full images in one evaluation. Since the whole detection pipeline is a single network, it can be optimized end-to-end directly on detection performance.
Our unified architecture is extremely fast. Our base YOLO model processes images in real-time at 45 frames per second. A smaller version of the network, Fast YOLO, processes an astounding 155 frames per second while still achieving double the mAP of other real-time detectors. Compared to state-of-the-art detection systems, YOLO makes more localization errors but is far less likely to predict false detections where nothing exists. Finally, YOLO learns very general representations of objects. It outperforms all other detection methods, including DPM and R-CNN, by a wide margin when generalizing from natural images to artwork on both the Picasso Dataset and the People-Art Dataset.read more
Citations
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Book ChapterDOI
Real-Time Retail Smart Space Optimization and Personalized Store Assortment with Two-Stage Object Detection Using Faster Regional Convolutional Neural Network
TL;DR: In this paper, the authors used state-of-the-art computer vision technology to address the issue of empty spaces created between products that might look sparse and lower stock display if not properly monitored.
Proceedings ArticleDOI
Box Detection and Positioning based on Mask R-CNN [1] for Container Unloading
TL;DR: This work uses deep learning-based target detection here, and the algorithm Mask-RCNN is used to detect specific objects and gets the recognition speed of each frame for 0.2 seconds, which can get the three-dimensional coordinates of each box precise.
Posted Content
Compressed Object Detection.
TL;DR: In this article, the authors extended pruning, a compression technique that discards unnecessary model connections, and weight sharing techniques for the task of object detection, without a loss in performance.
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Top-Related Meta-Learning Method for Few-Shot Object Detection.
TL;DR: Zhang et al. as mentioned in this paper proposed a Top-C classification loss (i.e., TCL-C) for classification task and a category-based grouping mechanism for categorybased meta-features obtained by the meta-model.
Proceedings ArticleDOI
Research on Detection Method of Traffic Anomaly Based on Improved YOLOv3
Xinwen Gao,Le Jiang +1 more
TL;DR: In this paper, a method for detecting abnormal traffic incidents is proposed to detect pedestrians on elevated roads, abnormal parking and reversing events in real-time, where the channel attention and spatial attention structure (CBAM) is embedded in front of each shortcut layer of the YOLOv3 basic network Darknet53.
References
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Proceedings ArticleDOI
Histograms of oriented gradients for human detection
Navneet Dalal,Bill Triggs +1 more
TL;DR: It is shown experimentally that grids of histograms of oriented gradient (HOG) descriptors significantly outperform existing feature sets for human detection, and the influence of each stage of the computation on performance is studied.
Journal ArticleDOI
ImageNet Large Scale Visual Recognition Challenge
Olga Russakovsky,Jia Deng,Hao Su,Jonathan Krause,Sanjeev Satheesh,Sean Ma,Zhiheng Huang,Andrej Karpathy,Aditya Khosla,Michael S. Bernstein,Alexander C. Berg,Li Fei-Fei +11 more
TL;DR: The ImageNet Large Scale Visual Recognition Challenge (ILSVRC) as mentioned in this paper is a benchmark in object category classification and detection on hundreds of object categories and millions of images, which has been run annually from 2010 to present, attracting participation from more than fifty institutions.
Posted Content
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
TL;DR: Faster R-CNN as discussed by the authors proposes a Region Proposal Network (RPN) to generate high-quality region proposals, which are used by Fast R-NN for detection.
Proceedings ArticleDOI
Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
TL;DR: RCNN as discussed by the authors combines CNNs with bottom-up region proposals to localize and segment objects, and when labeled training data is scarce, supervised pre-training for an auxiliary task, followed by domain-specific fine-tuning, yields a significant performance boost.
Proceedings ArticleDOI
Object recognition from local scale-invariant features
TL;DR: Experimental results show that robust object recognition can be achieved in cluttered partially occluded images with a computation time of under 2 seconds.