Lightweight Human Pose Estimation for Intelligent Anti-Cheating in Unattended Truck Weighing Systems

Abstract

Accurate human pose estimation is essential for anti-cheating detection in unattended truck scale systems, where human intervention must be reliably identified under challenging conditions such as poor lighting and small target pixel areas. This paper proposes a human joint detection system tailored for truck scale scenarios. To enable efficient deployment, several lightweight structures are introduced, among which an innovative channel hourglass convolution module is designed. By employing a channel compression-recover strategy, the module effectively reduces computational overhead while preserving network depth, significantly outperforming traditional grouped convolution and residual compression structures. In addition, a hybrid attention mechanism based on depthwise separable convolution is constructed, integrating spatial and channel attention to guide the network in focusing on key features, thereby enhancing robustness against noise interference and complex backgrounds. Ablation studies validate the optimal insertion position of the attention mechanism. Experiments conducted on the MPII dataset show that the proposed system achieves improvements of 8.00% in percentage of correct keypoints (PCK) and 2.12% in mean absolute error (MAE), alongside a notable enhancement in inference frame rate. The proposed approach promotes computational efficiency, system autonomy, and operational sustainability, offering a viable solution for energy-efficient, intelligent transportation systems, and long-term automated supervision in logistics and freight environments.