Patrol duty is a security and stability maintenance operation of great significance， but the patrol environment is complex， the object are diverse， and the problem of difficult detection is very prominent， so how to accurately and real-time detect patrol duty objects is of great practical significance.In order to improve the accuracy and real-time detection of patrol duty objects， the YOLOv5 algorithm is improved. In order to suppress the interference caused by the patrol environment， the ECA-Net attention mechanism is combined to improve the saliency of the detected object； and the introduction of BiFPN structure ensures better real-time performance and multi-scale object detection capabilities of the algorithm.Comparing the improved algorithm with the original algorithm， the mAP is improved by 3.51% ； comparing with four algorithms， the results show that the algorithm can better reduce the impact of patrol object detection of due to the problems of similar detection，diverse scales and light interference， which further verifies the effectiveness of the proposed algorithm in the task of patrol duty object detection.

Air-to-ground target detection plays an important role in performing typical military and civilian tasks on air-based unmanned platforms such as drones and missiles.Aiming at the problems of fewer effective features and high false detection rate of the air-to-ground target intelligent detection method,an air-to-ground target intelligent detection algorithm based on the dual attention mechanism is proposed.First,a channel attention mechanism is established,focusing on the important channels in the feature layer that contain the effective features of the target.Second,the spatial attention mechanism is used to enable the network to focus on local key target areas.The dual attention mechanism combines the advantages of the channel attention mechanism and the spatial attention mechanism to improve the efficiency of target detection.A loss function that couples the dual attention mechanism and target detection is also designed,which realizes the synchronous optimization of the loss function.Finally,a comparative experiment is performed using the air-to-ground target detection data set.The experimental results show that the proposed algorithm has higher target detection accuracy and training speed than other algorithms in the air-to-ground scenario.