DualYOLO: Remote Small Target Detection with Dual Detection Heads Based on Multi-scale Feature Fusion

In recent years, accurate and real-time long-range small target detection has become a popular and challenging task, particularly in time-sensitive scenarios such as unmanned aerial vehicle (UAV) scene analysis and military reconnaissance. Most existing solutions rely on deep CNNs to learn strong feature representations of objects isolated from the background to detect small objects with minimal visual features in images. However, this approach incurs significant computational overhead. In this paper, we propose DualYOLO, a fast and accurate long-range small object detection method that combines multi-level multi-scale feature fusion (MLMFF) and concat channel attention (CatCA). Specifically, in order to prevent small targets from becoming more and more blurred after multilayer convolution operations, DualYOLO fuses the features of different layers in the backbone network to obtain small target features with strong semantics and high detail. Furthermore, we use a new loss function to address the sensitivity of IoU to small object position deviations, thereby improving detection accuracy. In terms of data preprocessing, we utilize an image slicing strategy to process the dataset. The experimental results show that DualYOLO achieves 82.2% accuracy (in terms of mAP@.5) on the VEDAI dataset processed using slices, with a performance more than 2% higher than that of large models (e.g., YOLOv5x,YOLOR and YOLOv7).