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UAV aerial photos tend to have complicated backgrounds and dense targets that vary in size. Applying existing object detection algorithms to such images is often inaccurate and prone to misdetection and omission. To better improve the detection performance of UAV aerial photography, we proposed an improved small-target detection algorithm based on YOLOv5s: 1. We reconstructed the feature fusion network by introducing an upsampling layer, increasing the model&#x2019;s focus on features from small targets and improving related detection accuracy. 2. We introduced the SPD convolutional building block to downsample the feature map without losing learning information, improving the model&#x2019;s feature extraction ability. 3. We replaced the CIoU Loss function of the original model with EIoU to reduce the location loss during training and improve the regression accuracy. We experimented with the improved algorithm on the VisDrone2019 dataset and achieved mAP@0.5 of 44%, demonstrating a 10.7% improvement from the original model. The detection speed also increases to 99 FPS, indicating that the improved algorithm can maintain its real-time performance while improving its accuracy.

Improved YOLOv5s Algorithm for Small Target Detection in UAV Aerial Photography