CDPA-Net: An Indoor Work Sites Smoking Detection Framework Based on Contour-Driven Pose-Aware Feature Learning

Smoking detection in indoor work sites is challenging due to posture variability, object occlusion, poor lighting, and the small size of cigarettes. These factors hinder the extraction of reliable pose-aware features. Such features include hand–cigarette orientation and contours, which are critical for smoking detection. However, current mainstream detectors, such as YOLO-based methods, fail to capture pose-aware features under cluttered and low-visibility conditions. To address this, we propose the Contour-Driven Pose-Aware Network (CDPA-Net), which explicitly captures contour orientation and high-frequency appearance cues for robust smoking detection. Specifically, the Orientation-Driven Contour Extractor (ODCE) employs a Nonsubsampled Contourlet Transform to capture direction-sensitive posture and contour features, effectively suppressing background clutter. Additionally, the Frequency-Sensitive Attention Block (FSAB) highlights high-frequency discriminative signals under dim light via frequency-domain self-attention. Moreover, the Multi-Scale Frequency Integration Module (MFIM) fuses structural and spectral cues across scales to reinforce pose-aware representation. Experiments on both a public and a custom industrial dataset show that CDPA achieves 89.2% mAP50 at 112 FPS. This work provides a lightweight, interpretable, and accurate solution for smoking detection in industrial monitoring applications.