BFIDet: A YOLOv7-improved Vehicle and Pedestrian Detector via Balancing Feature Integration

Accurate vehicle and pedestrian detection are fundamental for safe driving and maintenance of traffic order. In this paper, a YOLOv7-improved vehicle and pedestrian detector via balancing feature integration (BFIDet) is proposed. First, EFFM module is designed to facilitate feature map fusion across layers. Second, GSRFConv is utilized to expand the receptive field of the intrinsic feature map as a way to improve the feature discriminability and robustness. VFBM module is then introduced to guide the propagation of the information flow as a way to solve the problem of dilution of features in non-adjacent layers and semantic differences between cross-scale features. In the experiments, the proposed method achieves 93.9% and 69.4% [email protected] and [email protected]:0.95 metric on the KITTI dataset, which are 2.1% and 1.5% better than YOLOv7, respectively, and the [email protected] metric on the SODA10M dataset reaches 63.1% with an improvement of 0.9% and 1.9% over YOLOv7 and YOLOv8m, respectively. The experimental results demonstrate that the proposed BFIDet is more accuracy than that of other mainstream models with controllable computational consumption.