Abstract: With the rapid development of technology,assisted driving technology has become an important direction for the future development of the automotive industry.In image-based road obstacle detection,existing methods have limited detection capabilities for targets with large scale changes,small targets,and targets with occlusion,often resulting in misjudgments and omissions.To address this problem,a road obstacle detection method based on self-attention and bidirectional feature fusion(CoXt-FCOS) is proposed.This method introduces a grouped self-attention mechanism module CoXT in the backbone to enhance the global information capture capabilities of the network.To solve the occlusion problem,a cross-stage pyramid pooling module SPPCSPC is introduced.In the feature fusion module,a path enhancement network is introduced,forming a bidirectional feature fusion module ESPAFPN,to enhance the network's perception of small targets.Experiments show that the CoXT-FCOS model has high accuracy,with an mAP of 88% on the CODA dataset,and can more accurately detect obstacles on the road.

Key words: Obstacle detection, Autopilot, Fully convolutional one-stage object detection, Self-attentio, Feature fusion