Foreign Bodies Detector Based on DETR for High-Resolution X-Ray Images of Textiles

Ding, Jiangang; Ye, Chao; Wang, Huaizhi; Huo, Ju; Yang, Ming; Li, Wei

doi:10.1109/tim.2023.3246510

Cited by 6 publications

(3 citation statements)

References 47 publications

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“…Chen et al [ 2 ] used Discrete Cosine Transform (DCT) to convert RGB domain images into frequency-domain representations, alongside proposing an RGB Frequency Attention Module (RFAM) for comprehensive feature representation integrating RGB and frequency-domain information. Ding et al [ 3 ] introduced FE-DETR, a transformer-based target detection framework that improves anchor-based detectors in foreign object detection through split-attention mechanisms, integration of DCN and CBAM, an MSFE module for feature dispersion processing, and a transformer as a prediction head, along with optimized training strategies to boost detector performance. Wei et al [ 4 ] employed collaborative knowledge distillation and leveraged a teacher model to assist the student model in distillation training, thereby uncovering hard-to-detect prohibited objects in X-ray images.…”

Section: Introductionmentioning

confidence: 99%

Fine-YOLO: A Simplified X-ray Prohibited Object Detection Network Based on Feature Aggregation and Normalized Wasserstein Distance

Zhou,

Cao,

et al. 2024

Sensors

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X-ray images typically contain complex background information and abundant small objects, posing significant challenges for object detection in security tasks. Most existing object detection methods rely on complex networks and high computational costs, which poses a challenge to implement lightweight models. This article proposes Fine-YOLO to achieve rapid and accurate detection in the security domain. First, a low-parameter feature aggregation (LPFA) structure is designed for the backbone feature network of YOLOv7 to enhance its ability to learn more information with a lighter structure. Second, a high-density feature aggregation (HDFA) structure is proposed to solve the problem of loss of local details and deep location information caused by the necked feature fusion network in YOLOv7-Tiny-SiLU, connecting cross-level features through max-pooling. Third, the Normalized Wasserstein Distance (NWD) method is employed to alleviate the convergence complexity resulting from the extreme sensitivity of bounding box regression to small objects. The proposed Fine-YOLO model is evaluated on the EDS dataset, achieving a detection accuracy of 58.3% with only 16.1 M parameters. In addition, an auxiliary validation is performed on the NEU-DET dataset, the detection accuracy reaches 73.1%. Experimental results show that Fine-YOLO is not only suitable for security, but can also be extended to other inspection areas.

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Section: Introductionmentioning

confidence: 99%

Fine-YOLO: A Simplified X-ray Prohibited Object Detection Network Based on Feature Aggregation and Normalized Wasserstein Distance

Zhou,

Cao,

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…As an alternative to convolutional neural networks, Transformer [5] has demonstrated good performance on some visual issues [6]. Swin Transformer [7] integrates the advantages of CNN and Transformer, showing great promise.…”

Section: Introductionmentioning

confidence: 99%

C-SwinIR: Dental CT super-resolution reconstruction fused with SwinIR

Wang,

Ding,

et al. 2023

J. Phys.: Conf. Ser.

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Super-resolution reconstruction (SR) of dental computed tomography (Dental CT) images is a innovative and challenging task. To address the limitations of Dental CT in obtaining high-resolution (HR) images due to equipment constraints and noise interference, we propose a Dental CT SR method called C-SwinIR based on SwinIR. Firstly, the self-calibrated convolutions network (SCNet) is introduced to solve the problem of detail loss in shallow feature graphs and improve the ability to recover details. Subsequently, the cross-shaped windows (CSWin) self-attention transformer structure is used to replace the original transformer structure, which improves the ability of the model to obtain context information. Eventually, the integration of efficient channel attention (ECA-Net) module effectively realizes the local cross-channel interaction, accelerates the model convergence and solves the problem of gradient explosion in training. Experimental results show that our proposed method is superior to the original SwinIR network by achieving a higher peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) by 0.287 and 0.003 respectively. Additionally, it decreases mean squared error (MSE) by 1.108. By using this method, clinicians can obtain clearer details and textures from Dental CT, which effectively assist in diagnosis.

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“…In recent years, deep learning has gradually matured in the automatic processing of X-ray image data [1]. In public places such as subways, stations and airports, the detection efficiency of traditional security inspection machines is slow and affected by human factors.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Security Contraband Detection through Integration of Attention Mechanism in R3Det

Ye,

Ding,

Wang

et al. 2023

J. Phys.: Conf. Ser.

Self Cite

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Detecting dangerous goods in security images is a challenging task. To overcome the challenges of localization difficulty and directional feature loss of contraband in X-ray images, our proposed solution, R3Det, employs the Convolutional Block Attention Module (CBAM). By integrating ResNeSt into the original detector, our detector includes a soft attention mechanism to redistribute weights among feature channels. This enhances the network’s ability to extract important features and facilitates extraction of target objects features under complex backgrounds. Subsequently, we introduced the spatial and channel attention mechanism during the connection between the backbone and the Feature Pyramid Network (FPN), enabling the model to focus on significant features while ignoring complex background information, then the following Feature Refinement Module to achieve feature alignment in a pixel-by-pixel manner. Our approach successfully achieved rotating target detection in the background of complex X-ray images. Through end-to-end training, our proposed method achieves a 2.6% improvement over the original detector, with a mean Average Precision (mAP) of 86.7%. Notably, our approach showed remarkable results in detecting sensors, pressure, and firetrackers. Now, we have deployed our proposed method on actual security machines for hazardous material detection tasks.

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Foreign Bodies Detector Based on DETR for High-Resolution X-Ray Images of Textiles

Cited by 6 publications

References 47 publications

Fine-YOLO: A Simplified X-ray Prohibited Object Detection Network Based on Feature Aggregation and Normalized Wasserstein Distance

Fine-YOLO: A Simplified X-ray Prohibited Object Detection Network Based on Feature Aggregation and Normalized Wasserstein Distance

C-SwinIR: Dental CT super-resolution reconstruction fused with SwinIR

Enhanced Security Contraband Detection through Integration of Attention Mechanism in R3Det

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