Multi-Dimensional Information Fusion You Only Look Once Network for Suspicious Object Detection in Millimeter Wave Images

Chen, Zhenhong; Tian, Ruijiao; Xiong, Di; Yuan, Chenchen; Li, Tang; Shi, Yiran

doi:10.3390/electronics13040773

Cited by 2 publications

(2 citation statements)

References 51 publications

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“…represent the centers of the two detection boxes, respectively. The calculation equation for ϵ is shown in Equations ( 8) and (9).…”

Section: Distance and Shape Lossmentioning

confidence: 99%

“…Zhao et al [ 8 ] have improved the YOLOv5 by making it lightweight to enable the rapid detection of sewer defects. Chen et al [ 9 ] used the YOLO model to achieve good results in suspicious object detection in millimeter wave images. Ding et al [ 10 ] improved the YOLOv5 model’s capacity for global feature fusion by integrating the EPMS module.…”

Section: Related Workmentioning

confidence: 99%

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OD-YOLO: Robust Small Object Detection Model in Remote Sensing Image with a Novel Multi-Scale Feature Fusion

Bu,

Ye,

Tie

et al. 2024

Sensors

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As remote sensing technology has advanced, the use of satellites and similar technologies has become increasingly prevalent in daily life. Now, it plays a crucial role in hydrology, agriculture, and geography. Nevertheless, because of the distinct qualities of remote sensing, including expansive scenes and small, densely packed targets, there are many challenges in detecting remote sensing objects. Those challenges lead to insufficient accuracy in remote sensing object detection. Consequently, developing a new model is essential to enhance the identification capabilities for objects in remote sensing imagery. To solve these constraints, we have designed the OD-YOLO approach that uses multi-scale feature fusion to improve the performance of the YOLOv8n model in small target detection. Firstly, traditional convolutions have poor recognition capabilities for certain geometric shapes. Therefore, in this paper, we introduce the Detection Refinement Module (DRmodule) into the backbone architecture. This module utilizes Deformable Convolutional Networks and the Hybrid Attention Transformer to strengthen the model’s capability for feature extraction from geometric shapes and blurred objects effectively. Meanwhile, based on the Feature Pyramid Network of YOLO, at the head of the model framework, this paper enhances the detection capability by introducing a Dynamic Head to strengthen the fusion of different scales features in the feature pyramid. Additionally, to address the issue of detecting small objects in remote sensing images, this paper specifically designs the OIoU loss function to finely describe the difference between the detection box and the true box, further enhancing model performance. Experiments on the VisDrone dataset show that OD-YOLO surpasses the compared models by at least 5.2% in mAP50 and 4.4% in mAP75, and experiments on the Foggy Cityscapes dataset demonstrated that OD-YOLO improved mAP by 6.5%, demonstrating outstanding results in tasks related to remote sensing images and adverse weather object detection. This work not only advances the research in remote sensing image analysis, but also provides effective technical support for the practical deployment of future remote sensing applications.

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“…represent the centers of the two detection boxes, respectively. The calculation equation for ϵ is shown in Equations ( 8) and (9).…”

Section: Distance and Shape Lossmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

OD-YOLO: Robust Small Object Detection Model in Remote Sensing Image with a Novel Multi-Scale Feature Fusion

Bu,

Ye,

Tie

et al. 2024

Sensors

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FA‐UNet: Semantic Segmentation of Passive Millimeter–Wave Images for Concealed Object Detection

Zhang,

Guo,

et al. 2024

International Journal of RF and Microwave Computer-Aided Engine

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Passive millimeter–wave (PMMW) scanners are widely used for personal security screening in public places due to their nonradiation and high real‐time capabilities. However, the images obtained by these scanners frequently exhibit low signal‐to‐noise ratios and contrast, presenting challenges for automated detection systems. To address this issue, we propose an efficient semantic segmentation approach, FA‐UNet, that employs a UNet architecture with a fusion attention mechanism to conduct binary classification (human body vs. background, including objects) for PMMW images. This approach incorporates a spatial attention mechanism into the lateral connections between the encoder and decoder and introduces a channel attention mechanism during the feature fusion process in the decoder. By combining these attention mechanisms, FA‐UNet leads to more precise segmentation outcomes. The segmented image is then fused with the original image using our multistage fusion method, in which, first, the two images are blended in a 1:1 ratio for object detection. Then, a new fused image is obtained by adjusting the ratio within a certain range (0.3–0.5). Finally, the object detection results are overlaid on this fused image to generate a directly displayable image. We evaluate our method using a self‐made dataset. Experimental results demonstrate that FA‐UNet can accurately segment the human body region and preserve object shapes effectively. Using the fused image for object detection helps reduce false detections caused by background noise interference while improving the detection rate of weak targets. Additionally, the fused image aids in manual image interpretation in locations with higher security inspection levels and contributes to protect the privacy of individuals undergoing inspection to the greatest extent possible.

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Multi-Dimensional Information Fusion You Only Look Once Network for Suspicious Object Detection in Millimeter Wave Images

Cited by 2 publications

References 51 publications

OD-YOLO: Robust Small Object Detection Model in Remote Sensing Image with a Novel Multi-Scale Feature Fusion

OD-YOLO: Robust Small Object Detection Model in Remote Sensing Image with a Novel Multi-Scale Feature Fusion

FA‐UNet: Semantic Segmentation of Passive Millimeter–Wave Images for Concealed Object Detection

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