UAV detection technology research based on deep learning

Abstract In the process of UAV target recognition, the target is small, the feature is not obvious, and the recognition accuracy is low, a UAV target detection method called YOLOv8-UD is proposed based on YOLOv8. This method replaces downsampling layers with SPD convolutions to mitigate fine-grained feature loss issues. It employs a BiFPN structure to get information which is multi-scale, enhancing representation of global semantic message and Improve awareness of small goals. As a result, to target detection, it significantly enhances the correctness and effectiveness. Additionally, to address difficulty of detecting low-resolution small targets, the network structure was modified by adding a detection head, for small target, thereby enhancing capability to detect it. Combining an improved loss function based on Inner-IoU accelerated model convergence and improved detection accuracy of samples at high IoU thresholds. We can tell by experiments that The algorithm proposed in this paper have 0.805 and 0.334 in mAP(0.5) and mAP(0.5:0.95) metrics, respectively. Compared to YOLOv8, improvements of 4.6% and 7% are achieved in mAP(0.5) and mAP (0.5:0.95) metrics. The experiments validate that the proposed way significantly enhances accuracy to UAV small target detection.