Abstract: Aiming at the problem of model misdetection and omission caused by the few pixels of long-distance intrusion object， insufficient texture information as well as large-scale transformations during the continuous approach of the object in regional security defence， an improved multi-scale moving object detection algorithm based on the YOLOv7-tiny algorithm is proposed. Firstly， a new OBM module is proposed for the feature extraction network， using a multi-dimensional attention mechanism to improve the feature extraction capability of the network. Secondly， an improved AC-BiFPN bidirectional feature fusion strategy is used to combine the multi-dimensional adaptive weighted fusion. The scale features are passed to the ACmix attention mechanism to improve the model’s perception of multi-scale objects. Finally， the activation function of the model is optimized to weight the area between the predicted frame and the real frame to reduce the model prediction bias. The model is tested on a self-made pedestrian and vehicle data set， and the experimental results show that compared with the original YOLOv7-tiny model， the improved YOLOv7-tiny model addresses the problem of misdetection and omission of pedestrians and vehicles during long-distances monitoring， with an increase of 3.96 percentage points in the detection accuracy， an increase of 2.22 percentage points in the average detection accuracy （mAP@0.5:0.95） ， and the real-time frame rate reaches 32.7 fps on edge GPUs， which meets the practical application requirements.

Key words: security defense, multi-scale object detection, YOLOv7-tiny, OBM, AC-BiFPNz