Small Object Detection Method for UAV Remote Sensing Images Based on αS-YOLO

In the domain of remote sensing imagery, minimizing the missed and false detection of small objects holds significant importance for the technology used in detecting small targets with unmanned aerial vehicles. In small object detection, accurately localizing these objects becomes challenging due to the varying motion amplitudes required in different directions. In addition, capturing the global dependency of features while keeping the network light poses a challenge. To tackle these challenges, we propose a novel object detection framework built upon the YOLO architecture, called αS-YOLO, which simultaneously tackles the problem of precise localization of small targets and maintaining the network's lightweight structure during long-range dependency capture. First, we design a new cross-convolution with 2 filters_ global context and efficient channel attention module, which aggregates global contextual features into the features of each pixel, enhancing the ability to model long-range dependencies while reducing network parameters to keep the network lightweight. Finally, to address the challenge of accurate small object localization, we propose a novel loss function, α-SIOU, which includes an adaptive angular control coefficient. This coefficient adjusts the distance loss in different directions based on the angular variation between the predicted and ground truth frames, adaptively converging the distance with the highest gradient. Experiments show that compared with the most advanced YOLO model, the accuracy of the proposed αS-YOLO method can be achieved in the detection of small targets on vehicles. On the VisDrone-DET2019 dataset, mAP and mAP50 compared to YOLOV8 improved 1% and 1.1%.