To address the problems of large truck blind-zone scope, complex background, large variation of target scale, poor effect of existing truck blind spot detection methods, and easy-to-miss recognition, an improved YOLOv8n truck blind zone target-detection algorithm is proposed. A mixed local channel attention module is added to the backbone network to improve the local spatial feature extraction capability of the network. The feature fusion network is improved by the scale sequence feature fusion module to fuse the deep semantic information of multiple scales of the image, the triple feature encoding module was used to capture the local details of the target, and Inner-CIoU is adopted as the bezel loss function to improve border detection accuracy. The experimental results show that on the self-built vehicle–pedestrian dataset, the proposed algorithm has a 3.14% better average detection accuracy than the traditional YOLOv8n algorithm, as well as better target detection in the blind zone of trucks.