Accurate recognition of UAVs on multi-scenario perception with YOLOv9-CAG

As the application of Unmanned Aerial Vehicles(UAVs) becomes increasingly widespread, the identification of UAVs is of great significance in the field of security. The research of advanced identification technology can effectively deal with the illegal invasion of UAVs and reduce the threat to aviation safety. However, during the recognition process, the effectiveness of UAVs identification is often compromised in long-distance and complex environments, particularly in night-time scenarios, where accurately and reliably identifying UAVs remains a significant challenge. To overcome this, this paper proposes an improved algorithm named YOLOv9-CAG, which is based on data from multiple sensors. This algorithm integrates the detection capabilities of visible light, infrared, and audio signals. The improvements primarily encompass three key aspects: The RepNSCPELAN4 module at the end of the trunk has been replaced with a CAM context feature enhancement module to bolster the capability of extracting features from small target UAVs; A GAM attention mechanism has been integrated into the head network to enhance the model's focus on specific areas or features of UAVs; An enhanced AKConv dynamic convolution has been implemented at the end of the head, building upon the original RepNSCPELAN4 module to more effectively capture contour details. On the Bird-UAV visible light data set, the mAP0.50 of UAVs by the improved YOLOV9-CAG model is 92.0%, which is 10.8% higher than that of the original YOLOv9 model. In terms of infrared data set, the mAP0.50 and recall of the enhanced model on the UAVs reached 86.5% and 89.2% respectively, which were also increased by 12.4% and 11.4% compared with the original YOLOv9 model, which expanded the effectiveness of the model in the infrared scene. On the audio spectrum dataset, the enhanced model demonstrated improvements in UAV recognition compared to the original YOLOv9 model, achieving increases of 8.4% in mAP0.50 and 14.3% in recall respectively. At the same time, the enhanced model in this study also has a good recognition effect on birds, achieving mAP0.50 of 85% and 94.8% under visible light and infrared conditions respectively, which is 19.8% and 1.1% higher than the original YOLOv9 model. In validation on real-world visible-light and infrared videos, the YOLOv9-CAG model demonstrated an overall average accuracy improvement of 6.8% and 3.8%, respectively, over the original YOLOv9 model. The results show that the improved YOLOv9-CAG model has excellent performance in UAVs recognition in multiple scenarios. This work pioneers a multimodal UAVs detection framework that significantly improves identification accuracy in challenging conditions, pushing the boundaries of UAVs identification technology.