BWFER-YOLOv8: An Enhanced Cascaded Framework for Concealed Object Detection
Contact-free concealed object detection using passive millimeter-wave imaging (PMMWI) sensors is a challenging task due to a low signal-to-noise ratio (SNR) and nonuniform illumination affecting the captured image’s quality. The nonuniform illumination also generates a higher false positive rate due...
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MDPI AG
2025-01-01
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| Online Access: | https://www.mdpi.com/2076-3417/15/2/690 |
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| author | Khalid Ijaz Ikramullah Khosa Ejaz A. Ansari Syed Farooq Ali Asif Hussain Faran Awais Butt |
| author_facet | Khalid Ijaz Ikramullah Khosa Ejaz A. Ansari Syed Farooq Ali Asif Hussain Faran Awais Butt |
| author_sort | Khalid Ijaz |
| collection | DOAJ |
| description | Contact-free concealed object detection using passive millimeter-wave imaging (PMMWI) sensors is a challenging task due to a low signal-to-noise ratio (SNR) and nonuniform illumination affecting the captured image’s quality. The nonuniform illumination also generates a higher false positive rate due to the limited ability to differentiate small hidden objects from the background of images. Several concealed object detection models have demonstrated outstanding performance but failed to combat the above-mentioned challenges concurrently. This paper proposes a novel three-stage cascaded framework named BWFER-YOLOv8, which implements a new alpha-reshuffled bootstrap random sampling method in the first stage, followed by image reconstruction using an adaptive Wiener filter in the second stage. The third stage uses a novel FER-YOLOv8 architecture with a custom-designed feature extraction and regularization (FER) module and multiple regularized convolution (Conv_Reg) modules for better generalization capability. The comprehensive quantitative and qualitative analysis reveals that the proposed framework outperforms the state-of-the-art tiny YOLOv3 and YOLOv8 models by achieving 98.1% precision and recall in detecting concealed weapons. The proposed framework significantly reduces the false positive rate, by up to 1.8%, in the detection of hidden small guns. |
| format | Article |
| id | doaj-art-35da8c24d5ff431596092160c12c8e07 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-35da8c24d5ff431596092160c12c8e072025-01-24T13:20:28ZengMDPI AGApplied Sciences2076-34172025-01-0115269010.3390/app15020690BWFER-YOLOv8: An Enhanced Cascaded Framework for Concealed Object DetectionKhalid Ijaz0Ikramullah Khosa1Ejaz A. Ansari2Syed Farooq Ali3Asif Hussain4Faran Awais Butt5Department of Electrical and Computer Engineering, COMSATS University Islamabad, Lahore 54000, PakistanDepartment of Electrical and Computer Engineering, COMSATS University Islamabad, Lahore 54000, PakistanDepartment of Electrical and Computer Engineering, COMSATS University Islamabad, Lahore 54000, PakistanDepartment of Computer Science, School of Systems and Technology, University of Management and Technology, Lahore 54770, PakistanDepartment of Electrical Engineering, School of Engineering, University of Management and Technology, Lahore 54770, PakistanCenter for Communication Systems and Sensing, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi ArabiaContact-free concealed object detection using passive millimeter-wave imaging (PMMWI) sensors is a challenging task due to a low signal-to-noise ratio (SNR) and nonuniform illumination affecting the captured image’s quality. The nonuniform illumination also generates a higher false positive rate due to the limited ability to differentiate small hidden objects from the background of images. Several concealed object detection models have demonstrated outstanding performance but failed to combat the above-mentioned challenges concurrently. This paper proposes a novel three-stage cascaded framework named BWFER-YOLOv8, which implements a new alpha-reshuffled bootstrap random sampling method in the first stage, followed by image reconstruction using an adaptive Wiener filter in the second stage. The third stage uses a novel FER-YOLOv8 architecture with a custom-designed feature extraction and regularization (FER) module and multiple regularized convolution (Conv_Reg) modules for better generalization capability. The comprehensive quantitative and qualitative analysis reveals that the proposed framework outperforms the state-of-the-art tiny YOLOv3 and YOLOv8 models by achieving 98.1% precision and recall in detecting concealed weapons. The proposed framework significantly reduces the false positive rate, by up to 1.8%, in the detection of hidden small guns.https://www.mdpi.com/2076-3417/15/2/690concealed object detectionpassive millimeter wavebootstrap methodfeature extractionregularizationYOLOv8 |
| spellingShingle | Khalid Ijaz Ikramullah Khosa Ejaz A. Ansari Syed Farooq Ali Asif Hussain Faran Awais Butt BWFER-YOLOv8: An Enhanced Cascaded Framework for Concealed Object Detection Applied Sciences concealed object detection passive millimeter wave bootstrap method feature extraction regularization YOLOv8 |
| title | BWFER-YOLOv8: An Enhanced Cascaded Framework for Concealed Object Detection |
| title_full | BWFER-YOLOv8: An Enhanced Cascaded Framework for Concealed Object Detection |
| title_fullStr | BWFER-YOLOv8: An Enhanced Cascaded Framework for Concealed Object Detection |
| title_full_unstemmed | BWFER-YOLOv8: An Enhanced Cascaded Framework for Concealed Object Detection |
| title_short | BWFER-YOLOv8: An Enhanced Cascaded Framework for Concealed Object Detection |
| title_sort | bwfer yolov8 an enhanced cascaded framework for concealed object detection |
| topic | concealed object detection passive millimeter wave bootstrap method feature extraction regularization YOLOv8 |
| url | https://www.mdpi.com/2076-3417/15/2/690 |
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