Environment-aware Strategy for Multi-sensor Fusion Based on Improved YOLO Networks

The environment perception technology of patrol robots within park areas faces challenges such as low accuracy in detecting small-scale targets and high false positive and false negative rates in target detection under low-light conditions. This paper addresses these issues by proposing several effective optimization methods. Extensive extensions and improvements are conducted on mainstream network models using the ApolloScape dataset to further enhance the accuracy of multi-target recognition within park environments. To overcome the limitations of a single sensor, a perception strategy based on the fusion of 3D and 2D detectors' image results is designed. The 3D detector adopts an improved Complex-YOLOv4 network, while the 2D detector uses an enhanced YOLOv5s network, primarily focusing on vehicles and pedestrians. Decision-level fusion of the two sensors effectively reduces false positives and false negatives in target detection under adverse environmental conditions. Experimental tests demonstrate that the proposed perception method achieves good real-time performance and accuracy in normal, adverse, and nighttime conditions, showcasing high robustness to changes in external environments.