Multi-Target Detection Algorithm for Fusion Images Based on an Attention Mechanism

Due to the inherent limitations of visible-light sensors, monitoring systems that rely solely on single-modal visible-light images exhibit reduced accuracy, posing safety concerns in applications such as autonomous driving. Infrared and visible-light image fusion technology addresses this issue by generating composite images that integrate complementary information from both modalities, thereby enhancing perception robustness. This study focuses on target detection in fused images. Given that targets in such images are often small and severely occluded, we propose an optimized detection framework to overcome these challenges. Specifically, we improve the YOLOv8 baseline model by introducing a dedicated small-object detection layer, incorporating the Global Attention Mechanism (GAM), and refining the loss function. Experimental results show that our method achieves a 5.0% improvement in mAP and a 6.5% gain in recall over the original YOLOv8. Furthermore, comparative experiments on fused and single-modal inputs demonstrate that fused images yield the highest detection accuracy. These results confirm that leveraging fused inputs significantly enhances detection accuracy and robustness in complex environments.