Research on a Lightweight YOLOv9 Object Detection Algorithm Fused with Adaptive Gated Coordinate Attention

Safety gear detection in complex industrial environments faces challenges such as strong background interference, multi-scale spatial perturbations, and the loss of small target features. Furthermore, existing attention-based object detection methods often struggle to balance fine-grained feature retention with background noise suppression. To address these issues, this paper proposes AGCA-YOLOv9, a lightweight object detection model (9.77 M parameters and 39.6 GFLOPs). The core contribution is the Adaptive Gated Coordinate Attention (AGCA) module integrated into the GELAN backbone. Unlike standard coordinate attention mechanisms, AGCA employs a dual-path hybrid pooling strategy combined with an adaptive gated weight fusion mechanism. This design dynamically regulates the synergy between global semantic information and local salient textures, differentiating it from traditional linear feature aggregation. Consequently, it effectively suppresses false detections caused by visually isomorphic backgrounds, such as dense steel frames, while enhancing the representation of distant tiny targets. Validation on the Safety Helmet and Reflective Jacket dataset and the Helmet-Vest-Belt dataset shows that, compared to the YOLOv9s baseline, AGCA-YOLOv9 increases the mAP@50:95 on the Safety Helmet and Reflective Jacket dataset by 0.6% (reaching 80.9%) and the recall rate by 0.4% (reaching 91.9%). Specifically, the mAP@50:95 for the safety helmet category improves by 0.8%. On the Helmet-Vest-Belt dataset, the mAP@50:95 increases by 1.5% (reaching 60.5%). The single-image inference time is 4.6 ms. These results indicate that the proposed algorithm achieves a practical trade-off between detection accuracy and real-time processing speed, demonstrating its potential for safety compliance monitoring in industrial scenarios.