PBA-YOLOv7: An Object Detection Method Based on an Improved YOLOv7 Network

Abstract

Deep learning-based object detection methods address the problem of how to trade off the object detection accuracy and detection speed of the model. This paper proposes the PBA-YOLOv7 network algorithm, which is based on the YOLOv7 network, and first introduces the PConv, which lightens the ELAN module in the backbone network structure and reduces the number of parameters to improve the detection speed of the network and then designs and introduces the BiFusionNet network, which better aggregates the high-level semantic features and the low-level semantic features; and finally, on this basis, the coordinate attention mechanism is introduced to make the network focus on more critical features without increasing the model complexity. The coordinate attention mechanism is introduced to make the network focus more on important feature information and improve the feature expression ability of the network without increasing the model complexity. Experiments on the publicly available KITTI’s dataset show that the PBA-YOLOv7 network model significantly improves both detection accuracy and detection speed compared to the original YOLOv7 model, with 4% and 7.8% improvement in mAP0.5 and mAP0.5:0.95, respectively, and six frames improvement in FPS. The improved algorithm in this paper weighs the model’s detection accuracy and detection speed in the detection task. It performs well compared to other algorithms, such as YOLOv7 and YOLOv5l.