Aiming at the lack of automated and intelligent forensics methods in the aviation reconnaissance and evidence collection， an aviation forensics target detection algorithm based on the fusion of RetinaNet and SE is proposed to solve the problem of large changes in target scale and imbalance in data collection. The performance is further improved by introducing an attention mechanism through the SE （squeeze-excitations） module. The feature pyramid network（FPN） can effectively deal with the problem of large changes in target scale， Focal Loss can effectively deal with the imbalance of categories in dataset， the SE module introduces a channel attention mechanism to strengthen the feature map， and can further use the extracted channel correlation to enhance effective features and suppress ineffective features. Through simulation experiments， it is verified that the algorithm can improve the accuracy of target detection with a small amount of calculation increased， further enhance the characterization ability of the model， and effectively improve the efficiency of target detection， which can provide a reference for related engineering applications.