In response to the challenges posed by complex background interference, diverse vehicle types, and scarce target features in highway construction scenarios, an improved YOLOv5-based highway construction vehicle detection algorithm was proposed. First, a spatial-aware channel attention (SCA) mechanism was designed to dynamically adjust channel weights, thereby enhancing feature representation related to construction vehicles and effectively suppressing background interference. Secondly, an improved weighted cross layer feature fusion network (WCL-FFN) was introduced to replace the original path aggregation network (PAN), optimizing feature fusion, reducing computational redundancy, and improving multi-scale object detection performance. Furthermore, phantom operations are incorporated to reduce network computation and parameter size, thereby increasing detection efficiency. Lastly, an efficient intersection over union (EIoU) loss function was used to replace the traditional IoU loss function, improving bounding box regression accuracy, detection performance, and network convergence speed. To verify the effectiveness of the algorithm, experiments were conducted on a highway construction vehicle dataset. The results show that the average detection accuracy (mAP) of the improved YOLOv5 model reaches 90.5%. Compared to YOLOv5s, mAP is improved by 8.9%; compared to YOLOx, it is improved by 6.2%; and compared to YOLOv8, it is improved by 3.5%. The method demonstrates greater robustness in complex scenarios and provides an efficient solution for highway construction vehicle detection.