Recent Advances in Deep Learning for Object Detection

2019/08/10 by Xiongwei Wu, Wu, Xiongwei, Doyen Sahoo +3 · 2 citations
Computer Science · #Advanced Image and Video Retrieval Techniques #Advanced Neural Network Applications #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Multimedia (cs.MM) #Video Surveillance and Tracking Methods #cs.CV #cs.LG #cs.MM

paper · pdf · doi:10.48550/arxiv.1908.03673

arxiv created 2019/08/10 · openalex publication_date 2019/08/10 · arxiv updated 2019/08/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Object detection is a fundamental visual recognition problem in computer vision and has been widely studied in the past decades. Visual object detection aims to find objects of certain target classes with precise localization in a given image and assign each object instance a corresponding class label. Due to the tremendous successes of deep learning based image classification, object detection techniques using deep learning have been actively studied in recent years. In this paper, we give a comprehensive survey of recent advances in visual object detection with deep learning. By reviewing a large body of recent related work in literature, we systematically analyze the existing object detection frameworks and organize the survey into three major parts: (i) detection components, (ii) learning strategies, and (iii) applications & benchmarks. In the survey, we cover a variety of factors affecting the detection performance in detail, such as detector architectures, feature learning, proposal generation, sampling strategies, etc. Finally, we discuss several future directions to facilitate and spur future research for visual object detection with deep learning. Keywords: Object Detection, Deep Learning, Deep Convolutional Neural Networks

Citations

• Local Decorrelation For Improved Detection
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• MetaAnchor: Learning to Detect Objects with Customized Anchors
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• Compression of Deep Convolutional Neural Networks for Fast and Low Power Mobile Applications
• LSTD: A Low-Shot Transfer Detector for Object Detection
• Robust Real-Time Face Detection

Cited by

• Analysis of Deep-Learning Methods in an ISO/TS 15066-Compliant Human-Robot Safety Framework
• CBAM-STN-TPS-YOLO: Enhancing Agricultural Object Detection through Spatially Adaptive Attention Mechanisms

Related

• From classical techniques to convolution-based models: A review of object detection algorithms
• Object Detection with Deep Learning: A Review
• Recent Advances in Object Detection in the Age of Deep Convolutional Neural Networks
• Deep Learning for Generic Object Detection: A Survey
• Deep Learning for Generic Object Detection: A Survey