Open AccessPosted Content
You Only Look Once: Unified, Real-Time Object Detection
Reads0
Chats0
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
More filters
Journal ArticleDOI
Detection of broadleaf weeds growing in turfgrass with convolutional neural networks
TL;DR: Deep learning CNN (DL-CNN) models that are remarkably accurate at detection of broadleaf weeds in turfgrasses using in situ video input in conjunction with a smart sprayer are reported.
Proceedings ArticleDOI
Augmented Vehicular Reality: Enabling Extended Vision for Future Vehicles
TL;DR: This work explores a capability called Augmented Vehicular Reality (AVR), which broadens the vehicle's visual horizon by enabling it to share visual information with other nearby vehicles, but requires careful techniques to align coordinate frames of reference, and to detect dynamic objects.
Journal ArticleDOI
Deblending and classifying astronomical sources with Mask R-CNN deep learning
Colin J. Burke,Colin J. Burke,P. Aleo,P. Aleo,Yu Ching Chen,Yu Ching Chen,Xin Liu,Xin Liu,John R. Peterson,G. H. Sembroski,Joshua Yao-Yu Lin +10 more
TL;DR: A new deep learning technique to detect, classify, and deblend sources in multiband astronomical images, and finds that clean deblends are handled robustly during object masking, even for significantly blended sources.
Journal ArticleDOI
Multi-modal uniform deep learning for RGB-D person re-identification
TL;DR: A multi-modal fusion layer is designed to combine features extracted from both depth images and RGB images through the network with a uniform latent variable which is robust to noise, and optimize the fusion layer with two CNN networks jointly.
Journal ArticleDOI
Real-time gun detection in CCTV: An open problem.
Jose L. Salazar González,Carlos Zaccaro,Juan Antonio Álvarez-García,Luis Miguel Soria Morillo,Fernando Sancho Caparrini +4 more
TL;DR: A new dataset obtained from a real CCTV installed in a university and the generation of synthetic images are presented, resulting in a weapon detection model able to be used in quasi real-time CCTV improving the state of the art on weapon detection in a two stages training.
References
More filters
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.