Anomaly object detection in x-ray images with Gabor convolution and bigger discriminative RoI pooling

Ma, Chunjie; Li, Zhuo; Li, Jiafeng; Zhang, Yutong; Zhang, Jing

doi:10.1117/12.2625815

Cited by 4 publications

(3 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…ResNeXt [26] diverse feature representation can be learned. In recent years, ResNeXt has been increasingly applied to various industrial applications [27][28][29][30], and achieved excellent performance. However, ResNeXt is designed for the natural images.…”

Section: Feature Extractionmentioning

confidence: 99%

EAOD‐Net: Effective anomaly object detection networks for X‐ray images

et al. 2022

IET Image Processing

Self Cite

View full text Add to dashboard Cite

Anomaly object detection is the core technology in the application for X‐ray images. However, the accuracy of current X‐ray anomaly object detection method still needs to be improved. In this paper, an effective anomaly object detection network is proposed to improve the detection accuracy of anomaly object for X‐ray images. Firstly, learnable Gabor convolution layer, deformable convolution, and spatial attention mechanism are introduced to enhance the representative ability of features in ResNeXt. Then, dense local regression is applied to predict the offset of multiple dense boxes in region proposal to locate the object accurately. At last, bigger discriminative RoI pooling is proposed to classify the candidate boxes to improve the accuracy of object classification. Experimental results on the SIXray and OPIXray datasets show that compared with the state‐of‐the‐art methods, the proposed EAOD‐Net can achieve the competitive detection performance.

show abstract

Section: Feature Extractionmentioning

confidence: 99%

EAOD‐Net: Effective anomaly object detection networks for X‐ray images

et al. 2022

IET Image Processing

Self Cite

View full text Add to dashboard Cite

show abstract

“…In recent years, various deep learning-based object detection methods have emerged continuously, and the detection accuracy has been improved remarkably. Compared with the natural images, X-ray images have their unique characteristics [3][4][5], which are as follows: (1) Lack of texture information, but rich in edge and contour information. (2) Highly overlapped by adjacent objects.…”

Section: Introductionmentioning

confidence: 99%

“…(2) Highly overlapped by adjacent objects. (3) Mass surrounding objects and high similarity with other objects.…”

Section: Introductionmentioning

confidence: 99%

Prohibited Object Detection in X-ray Images with Dynamic Deformable Convolution and Adaptive IoU

et al. 2022

2022 IEEE International Conference on Image Processing (ICIP)

View full text Add to dashboard Cite

Due to the variety and complexity of objects in X-ray images, how to detect the prohibited items automatically and accurately is a challenging problem. In this paper, an X-ray image prohibited object detection method based on Dynamic Deformable Convolution (DyDC) and adaptive Intersection over Union (IoU) is proposed based on Cascade R-CNN framework. The main contributions are as follows. First, DyDC is proposed to cope with the diversity of the prohibited objects in X-ray images and to improve the feature representation capability. Then, adaptive IoU mechanism is proposed, which can dynamically adjust the IoU threshold during the training process to generate high quality proposals. The proposed method is extensively evaluated on two publicly available benchmark datasets, namely SIXray and OPIXray, and the experimental results show that it can achieve the state-of-the-art detection accuracy, compared with other existing methods.

show abstract

Advancements in machine learning techniques for threat item detection in X-ray images: a comprehensive survey

Singh,

Dhiraj

2024

Int J Multimed Info Retr

View full text Add to dashboard Cite

Anomaly object detection in x-ray images with Gabor convolution and bigger discriminative RoI pooling

Cited by 4 publications

References 6 publications

EAOD‐Net: Effective anomaly object detection networks for X‐ray images

EAOD‐Net: Effective anomaly object detection networks for X‐ray images

Prohibited Object Detection in X-ray Images with Dynamic Deformable Convolution and Adaptive IoU

Advancements in machine learning techniques for threat item detection in X-ray images: a comprehensive survey

Contact Info

Product

Resources

About