Resumen
There are many small objects in UAV images, and the object scale varies greatly. When the SSD algorithm detects them, the backbone network?s feature extraction capabilities are poor; it does not fully utilize the semantic information in the deeper feature layer, and it does not give enough consideration to the little items in the loss function, which result in serious missing object detection and low object detection accuracy. To tackle these issues, a new algorithm called RSAD (Resnet Self-Attention Detector) that takes advantage of the self-attention mechanism has been proposed. The proposed RSAD algorithm utilises the residual structure of the ResNet-50 backbone network, which is more capable of feature extraction, in order to extract deeper features from UAV image information. It then utilises the SAFM (Self-Attention Fusion Module) to reshape and concatenate the shallow and deep features of the backbone network, selectively weighted by attention units, ensuring the efficient fusion of features to provide rich semantic features for small object detection. Lastly, it introduces the Focal Loss loss function, which adjusts the corresponding parameters to enhance the contribution of small objects to the detection model. The ablation experiments show that the mAP of RSAD is 10.6% higher than that of the SSD model, with SAFM providing the highest mAP enhancement of 7.4% and ResNet-50 and Focal Loss providing 1.3% and 1.9% enhancements, respectively. The detection speed is only reduced by 3FPS, but it meets the real-time requirement. Comparison experiments show that in terms of mAP, it is far ahead of Faster R-CNN, Cascade R-CNN, RetinaNet, CenterNet, YOLOv5s, and YOLOv8n, which are the mainstream object detection models; In terms of FPS, it slightly inferior to YOLOv5s and YOLOv8n. Thus, RSAD has a good balance between detection speed and accuracy, and it can facilitate the advancement of the UAV to complete object detection tasks in different scenarios.