Towards Real-World Prohibited Item Detection: A Large-Scale X-ray Benchmark

By Boying Wang, Libo Zhang, Longyin Wen, Xianglong Liu, Yanjun Wu. 2021

118 citations recorded by the source corpus.

Automatic security inspection using computer vision technology is a challenging task in real-world scenarios due to various factors, including intra-class variance, class imbalance, and occlusion. Most of the previous methods rarely solve the cases that the prohibited items are deliberately hidden in messy objects due to the lack of large-scale datasets, restricted their applications in real-world scenarios. Towards real-world prohibited item detection, we collect a large-scale dataset, named as PIDray, which covers various cases in real-world scenarios for prohibited item detection, especially for deliberately hidden items. With an intensive amount of effort, our dataset contains 12 categories of prohibited items in 47, 677 X-ray images with high-quality annotated segmentation masks and bounding boxes. To the best of our knowledge, it is the largest prohibited items detection dataset to date. Meanwhile, we design the selective dense attention network (SDANet) to construct a strong baseline, which consists of the dense attention module and the dependency refinement module. The dense attention module formed by the spatial and channel-wise dense attentions, is designed to learn the discriminative features to boost the performance. The dependency refinement module is used to exploit the dependencies of multi-scale features. Extensive experiments conducted on the collected PIDray dataset demonstrate that the proposed method performs favorably against the state-of-the-art methods, especially for detecting the deliberately hidden items.

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition, Industrial and Manufacturing Engineering

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

1. Alexander Kirillov
2. Nikhila Ravi
3. Ross B. Girshick
4. Piotr Doll'ar
5. Alexander C. Berg

In Computer Vision and Pattern Recognition

1. Luc Van Gool
2. Jitendra Malik
3. Xiaogang Wang
4. Dacheng Tao
5. Trevor Darrell
6. Alan L. Yuille
7. Ming-Hsuan Yang
8. Ross B. Girshick

In Industrial and Manufacturing Engineering

1. Tete Xiao

Citations in the corpus

• Segment Anything (2023, methodology): “-20 0 +20 +40 IoU delta at 1 center point GTEA [34, 63] TrashCan [52] DRAM [24] PIDRay [104] Cityscapes [25] WoodScape [112] IBD [17] EgoHOS [113] Plittersdorf [46] VISOR [28, 27] NDISPark [22, 23] Hypersim [86] OVIS [81] ADE20K [117] iShape [111] ZeroWaste-f [6] STREETS [91] LVIS [44] NDD20 [100] T”
• SAM 2: Segment Anything in Images and Videos (2024, unknown): “For completeness, we list the 23 datasets: LVIS (Gupta et al., 2019), ADE20K (Zhou et al., 2019), Hypersim (Roberts et al., 2021), Cityscapes (Cordts et al., 2016), BBBC038v1 (Caicedo et al., 2019), DOORS (Pugliatti & Topputo, 2022), DRAM (Cohen et al., 2022), EgoHOS (Zhang et al., 2022), GTEA (Fath”