Airborne Small Target Detection Method Based on Multimodal and Adaptive Feature Fusion

The detection of airborne small targets amidst cluttered environments poses significant challenges. Factors such as the susceptibility of a single RGB image to interference from the environment in target detection and the difficulty of retaining small target information in detection necessitate the development of a new method to improve the accuracy and robustness of airborne small target detection. This article proposes a novel approach to achieve this goal by fusing RGB and infrared (IR) images, which is based on the existing fusion strategy with the addition of an attention mechanism. The proposed method employs the YOLO-SA network, which integrates a YOLO model optimized for the downsampling step with an enhanced image set. The fusion strategy employs an early fusion method to retain as much target information as possible for small target detection. To refine the feature extraction process, we introduce the self-adaptive characteristic aggregation fusion (SACAF) module, leveraging spatial and channel attention mechanisms synergistically to focus on crucial feature information. Adaptive weighting ensures effective enhancement of valid features while suppressing irrelevant ones. Experimental results indicate 1.8% and 3.5% improvements in mean average precision (mAP) over the LRAF-Net model and Infusion-Net detection network, respectively. Additionally, ablation studies validate the efficacy of the proposed algorithm's network structure.