用深度学习实现端到端实时高精度目标检测
Object Detection and Tracking
- 全深度学习端到端检测，摆脱依赖其他视觉算法
- 在年度挑战赛最困难公开数据集上训练，性能优异
- 适合需要快速精准检测的应用场景
高效准确的目标检测是计算机视觉系统发展中的重要课题。随着深度学习技术的出现，目标检测的准确率显著提升。本项目旨在将现代目标检测技术集成，实现高精度与实时性能的统一。许多目标识别系统依赖其他计算机视觉算法，导致性能差且效率低下，这是主要障碍。本研究完全采用深度学习技术解决端到端目标检测问题。网络在每年目标检测挑战赛中使用的最困难公开数据集上进行训练，相关应用可受益于该系统快速而精确的目标发现能力。
原文摘要 · Abstract (English)
Efficient and accurate object detection is an important topic in the development of computer vision systems. With the advent of deep learning techniques, the accuracy of object detection has increased significantly. The project aims to integrate a modern technique for object detection with the aim of achieving high accuracy with real-time performance. The reliance on other computer vision algorithms in many object identification systems, which results in poor and ineffective performance, is a significant obstacle. In this research, we solve the end-to-end object detection problem entirely using deep learning techniques. The network is trained using the most difficult publicly available dataset, which is used for an annual item detection challenge. Applications that need object detection can benefit the system's quick and precise finding.
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