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

Year: 2026
Type: article
Abstract: 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.
Source: Mathematics
DOI: 10.3390/math14091462
Language: English
Open Access status: gold
Estimated APC paid: $3,137
OpenAlex ID: W7156984591
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