YOLOv4-5D: An Effective and Efficient Object Detector for Autonomous Driving

• Published in: IEEE transactions on instrumentation and measurement Vol. 70; pp. 1 - 13
• Main Authors: Cai, Yingfeng, Luan, Tianyu, Gao, Hongbo, Wang, Hai, Chen, Long, Li, Yicheng, Sotelo, Miguel Angel, Li, Zhixiong
• Format: Journal Article
• Language: English
• Published: New York IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Algorithms; Autonomous driving; Autonomous vehicles; Computation; Computer networks; Convolution; CSPDarknet53; Datasets; Detectors; Feature extraction; Formability; Inference; Inference algorithms; Object detection; Object recognition; Pruning; Real time operation; Real-time systems; Roads; Scaling factors; YOLOv4
• DOI: 10.1109/TIM.2021.3065438
• ISSN: 0018-9456

The use of object detection algorithms has become extremely important in autonomous vehicles. Object detection at high accuracy and a fast inference speed is essential for safe autonomous driving. Therefore, the balance between the effectiveness and efficiency of the object detector must be considered. This article proposes a one-stage object detection framework for improving the detection accuracy while supporting a true real-time operation based on the YOLOv4. The backbone network in the proposed framework is the CSPDarknet53_dcn(P). The last output layer in the CSPDarknet53 is replaced with deformable convolution to improve the detection accuracy. In order to perform feature fusion, a new feature fusion module PAN++ is designed and five scales detection layers are used to improve the detection accuracy of small objects. In addition, this article proposes an optimized network pruning algorithm to solve the problem that the real-time performance of the algorithm cannot be satisfied due to the limited computing resources of the vehicle-mounted computing platform. The method of sparse scaling factor is used to improve the existing channel pruning algorithm. Compared to the YOLOv4, the YOLOV4-5D improves the mean average precision by 4.23% on the BDD data sets and 1.68% on the KITTI data sets. Finally, by pruning the model, the inference speed of YOLOV4-5D is increased 31.3% and the memory is only 98.1 MB when the detection accuracy is almost unchanged. Nevertheless, the proposed algorithm is capable of real-time detection at faster than 66 frames/s (fps) and shows higher accuracy than the previous approaches with a similar fps.