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<p>Detecting concealed objects, anatomical keypoints, and segmenting human body parts in backscatter millimeter-wave images are essential tasks for enhancing airport security and transportation safety. However, the deployment of reliable automated systems is challenged by factors such as poor image quality, scarce detailed annotations, and stringent privacy regulations. To address these issues, we introduce EfficientPoseSegNet, a hybrid deep learning framework designed for efficient annotation use, concealed object detection, and body-aware analysis in security screening images. The model leverages parallel EfficientNet and DenseNet backbones to capture rich multi-scale features from low-resolution scans, refined using a Convolutional Block Attention Module (CBAM) to enhance focus on critical anatomical areas while reducing background noise. Instead of directly predicting joint coordinates, EfficientPoseSegNet outputs spatial heatmaps representing the confidence of anatomical keypoints. These keypoints are then extracted via soft-argmax and used to segment the human body into 17 anatomical regions. To further improve robustness and generalization under weak supervision, we incorporate a task-aware, confidence-weighted adaptation of Stochastic Weight Averaging (SWA), which stabilizes training and enhances multi-task performance. Additionally, we integrate an anomaly detection module that leverages anatomical segmentation features to identify concealed objects in body regions, addressing the ultimate operational goal of airport security screening. Tested on the Transportation Security Administration Passenger Screening Dataset, the model achieves a test loss of 3.9335, mean absolute error of 1.4330 pixels, keypoint accuracy of 99.79% within a 10-pixel threshold, pose estimation accuracy of 99%, segmentation Intersection over Union (IoU) of 97%, and an average anomaly detection AUC of 0.94 across body regions. This approach contributes significantly to Transportation Science and Logistics by enabling scalable, privacy-compliant, and real-time human pose estimation, body part segmentation, and concealed object detection in fast-paced airport screening settings.</p>

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EfficientPoseSegNet: a weakly supervised, attention-guided framework for human pose estimation, anatomical segmentation, and concealed object detection in backscatter millimeter-wave security screening

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月5日

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  • Muhammad Zaheer Sajid,
  • Muhammad Fareed Hamid,
  • Imran Qureshi,
  • Muhammad Imran Sharif,
  • Nour Aburaed

摘要

Detecting concealed objects, anatomical keypoints, and segmenting human body parts in backscatter millimeter-wave images are essential tasks for enhancing airport security and transportation safety. However, the deployment of reliable automated systems is challenged by factors such as poor image quality, scarce detailed annotations, and stringent privacy regulations. To address these issues, we introduce EfficientPoseSegNet, a hybrid deep learning framework designed for efficient annotation use, concealed object detection, and body-aware analysis in security screening images. The model leverages parallel EfficientNet and DenseNet backbones to capture rich multi-scale features from low-resolution scans, refined using a Convolutional Block Attention Module (CBAM) to enhance focus on critical anatomical areas while reducing background noise. Instead of directly predicting joint coordinates, EfficientPoseSegNet outputs spatial heatmaps representing the confidence of anatomical keypoints. These keypoints are then extracted via soft-argmax and used to segment the human body into 17 anatomical regions. To further improve robustness and generalization under weak supervision, we incorporate a task-aware, confidence-weighted adaptation of Stochastic Weight Averaging (SWA), which stabilizes training and enhances multi-task performance. Additionally, we integrate an anomaly detection module that leverages anatomical segmentation features to identify concealed objects in body regions, addressing the ultimate operational goal of airport security screening. Tested on the Transportation Security Administration Passenger Screening Dataset, the model achieves a test loss of 3.9335, mean absolute error of 1.4330 pixels, keypoint accuracy of 99.79% within a 10-pixel threshold, pose estimation accuracy of 99%, segmentation Intersection over Union (IoU) of 97%, and an average anomaly detection AUC of 0.94 across body regions. This approach contributes significantly to Transportation Science and Logistics by enabling scalable, privacy-compliant, and real-time human pose estimation, body part segmentation, and concealed object detection in fast-paced airport screening settings.