Road debris poses a significant threat to traffic safety, particularly small-scale objects that are difficult to detect under complex road conditions. Traditional manual inspection methods suffer from low efficiency, limited coverage, and delayed response. To address these challenges, this study proposes a lightweight mobile monitoring system for small-scale road debris detection based on vehicle-mounted sensing and dynamic data transmission. The system integrates a lightweight image acquisition device, a self-developed mobile monitoring software, and an improved lightweight detection model termed Dynamic-YOLOv8n. The model introduces dynamic convolution and attention mechanisms to enhance feature representation while maintaining computational efficiency. In addition, a dynamic compression transmission mechanism is designed to adaptively adjust image compression and transmission frequency under fluctuating network bandwidth, ensuring stable data transfer between the front-end terminal and the backend platform.<br />A dedicated debris dataset was constructed from field-collected images covering diverse road types and environmental conditions. Experimental results show that the proposed model achieves a mean Average Precision (mAP) of 93.2% while maintaining real-time detection performance. Field tests conducted on urban road networks further demonstrate that the system significantly improves detection efficiency compared with manual inspection and enables reliable real-time monitoring of road debris. The proposed framework provides an effective solution for intelligent road inspection and digital road maintenance.