ACP-YOLO: Asymmetric Center Point Bounding Box Regression Strategy and Angle Loss-based YOLO for Object Detection

Abstract

The YOLOv4 approach has gained significant popularity in industrial object detection due to its impressive real-time processing speed and relatively favorable accuracy. However, it has been observed that YOLOv4 faces challenges in accurately detecting small objects. Its bounding box regression strategy is rigid and fails to effectively leverage the asymmetric characteristics of objects, limiting its ability to enhance object detection accuracy. This paper proposes an enhanced version of YOLOv4 called ACP-YOLO (asymmetric center point bounding box regression-based YOLOv4). The ACP-YOLO approach introduces two customized modules: an asymmetric center point bounding box regression strategy and an angle-loss function. These modules contribute to improving the algorithm's detection accuracy by enabling more precise localization of objects. Additionally, ACP-YOLO adopts an improved feature fusion technique, which facilitates enhanced information flow within the network, thereby further enhancing accuracy performance. Experimental evaluations conducted on the COCO2017 dataset demonstrate the effectiveness of the proposed method. ACP-YOLO achieves an average precision of 45.6\%, surpassing both YOLOv4 and certain previously developed one-stage detectors. The utilization of an asymmetric center point bounding box regression strategy and the incorporation of robust feature fusion contribute to superior object detection accuracy in ACP-YOLO compared to YOLOv4.