Damage assessment algorithm based on deep learning and fuzzy analytic hierarchy process

To address the detection and assessment of military target damage, this paper proposes a two-stage damage assessment method based on deep learning and fuzzy analytic hierarchy process. Firstly, in the Yolov5-based dual-stage target detection subsystem with coordinate attention mechanism, a key region extraction mechanism is employed to extract damage components, and a damage component classifier based on the combination of intersection over union, Hungarian linear matching, and decision tree is used for classification and quantification of damage severity. Then, in the damage assessment subsystem based on the triangular fuzzy analytic hierarchy process, multiple damage assessment weight systems and damage tree criteria are designed to comprehensively consider the damage features and categories extracted from the previous stage, achieving online real-time damage assessment for targets. Experimental results show that on various simulated datasets with multiple interference factors, the Yolov5-based dual-stage target detection subsystem with the attention mechanism achieves an average improvement in detection accuracy of over 3.6% when compared to classical target fine-grained recognition algorithms. It demonstrates better performance in target key region extraction, and damage classification and assessment, providing strong support and reference for target damage assessment and military operation decision making.