A Small Target Recognition Method Based on an Improved YOLOv8

The recognition of small-size targets presents a significant challenge in computer vision, as their reduced dimensions often lead to diminished detection accuracy. To address this issue, this paper proposes an enhanced small-size target recognition method based on an improved version of You Only Look Once Version 8 (YOLOv8). The proposed improvements include integrating the dynamic attention mechanism of BiFormer (Vision Transformer with Bi-level Routing Attention), which leverages the sparsity of dynamic and query perception to enable more flexible and adaptive content perception. Additionally, the Weighted Intersection over Union (WIOU) loss function is introduced to address the imbalance in Bounding Box Regression (BBR) between samples, enhancing the overall accuracy of the model. Furthermore, a specialized detection head for small targets and a confidence-adaptive module are added at the detection head’s end, improving feature extraction and continuous tracking capabilities for small targets, especially under conditions of low visibility and target occlusion. Experimental results demonstrate that the improved model significantly enhances the detection of incomplete and small-sized targets, providing robust performance in scenarios with occlusion and reduced visibility. This study emphasizes the potential of the enhanced YOLOv8 model in real-world applications, providing new improvement ideas for the development of occluded and small target recognition.