STSODNet: Scale Transformer Small Object Detection Network

Dense small object detection in complex scenes is a valuable and challenging research field. While deep learning has driven significant advancements in computer vision, traditional object detection models still struggle to achieve high accuracy in detecting small objects, particularly in large-scale aerial images. Challenges such as scale variations, occlusions, and complex backgrounds continue to hinder the effective detection of dense small objects. In this paper, we present the Scale Transformer Small Object Detection Network (STSODNet), a novel architecture designed to address these challenges. First, we conceptualize the pronounced scale variation in drone images as an anomalous disturbance and propose a multiscale feature enhancement module (MSFEM), built upon the Spatial Transformer Network, to mitigate this effect. The multiscale feature enhancement module performs learnable, multi-point magnification on regions surrounding objects based on spatial saliency, enhancing the model’s scale invariance. Second, to generate a more accurate global saliency map and heighten the model’s focus on small target regions, we introduce a refined spatial attention mechanism, termed Spatial Region Attention. This mechanism combines coarse region attention with fine spatial attention to produce a more detailed saliency map and improve long-range dependency capture. Third, to achieve more accurate spatial regression of small objects, the traditional three-layer detection head is improved by expanding its output layer, resulting in a finer and larger output while maintaining the same number of parameters. Extensive experiments on the VisDrone and SeaPerson benchmark datasets validate that STSODNet achieves superior precision and robustness, outperforming current state-of-the-art object detection methods for small object detection.