Image-oriented multi-scale object detection algorithms often have the problem of mutual restriction between detection accuracy and system cost. Therefore, a lightweight and high-precision object detection algorithm based on YOLO framework is proposed. Under the YOLO framework, the mechanism of down-sampling and channel attention based on MobileNetv3 network is improved to accurately extract target features and reduce unnecessary overhead. The feature pyramid and single-stage headless fusion structure are designed, and different receptive fields are constructed to obtain different scale information, so as to enhance the adaptability of the algorithm for multi-scale targets. At the same time, SIOU is used as regression loss and Soft-NMS is used for redundant frame processing to improve the accuracy of the algorithm. Experiments are conducted on the MS COCO and UA-DETRAC. Compared with the original YOLOXs, the results show that the proposed improved algorithm reduces the number of model parameters and the computational cost reduced by 64.98% and 57.14% without reducing the accuracy. On the UA-DETRAC, mAP@0.5 reaches 70.5% which is improved by 3.52%, and FPS increases by 14.4%. The experimental results show that our algorithm greatly reduces the system overhead, improves the accuracy, and effectively guarantees the dual performance of detection.