Multi-Target Prohibited Item Recognition Algorithm for X-Ray Security Scene

Yang Cao; Li Zhang; Junxi Meng; Qian Song; Letian Zhang

doi:10.3788/LOP202259.1015009

[1] Liu W, Anguelov D, Erhan D et al. SSD: single shot MultiBox detector[M]. Leibe B, Matas J, Sebe N, et al. Computer vision-ECCV 2016, 9905, 21-37(2016).

[2] Akcay S, Kundegorski M E, Willcocks C G et al. Using deep convolutional neural network architectures for object classification and detection within X-ray baggage security imagery[J]. IEEE Transactions on Information Forensics and Security, 13, 2203-2215(2018).

[3] Liu J Y, Leng X X, Liu Y. Deep convolutional neural network based object detector for X-ray baggage security imagery[C], 1757-1761(2019).

[4] Miao C J, Xie L X, Wan F et al. SIXray: a large-scale security inspection X-ray benchmark for prohibited item discovery in overlapping images[C], 2114-2123(2019).

[5] Xu M S, Zhang H G, Yang J F. Prohibited item detection in airport X-ray security images via attention mechanism based CNN[M]. Lai J H, Liu C L, Chen X L, et al. Pattern recognition and computer vision, 11257, 429-439(2018).

[6] Gómez D, Yáñez J, Guada C et al. Fuzzy image segmentation based upon hierarchical clustering[J]. Knowledge-Based Systems, 87, 26-37(2015).

[7] An J Y, Zhang H G, Zhu Y et al. Semantic segmentation for prohibited items in baggage inspection[M]. Cui Z, Pan J S, Zhang S S, et al. Intelligence science and big data engineering. visual data engineering. Lecture notes in computer science, 11935, 495-505(2019).

[8] Bao H L, Wan M, Liu Z X et al. Real-time semantic segmentation network based on regional self-attention[J]. Laser & Optoelectronics Progress, 58, 0810018(2021).

[9] Cheng X Y, Zhao L Z, Hu Q et al. Real-time semantic segmentation based on dilated convolution smoothing and lightweight up-sampling[J]. Laser & Optoelectronics Progress, 57, 021017(2020).

[10] Shelhamer E, Long J, Darrell T. Fully convolutional networks for semantic segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 640-651(2017).

[11] Noh H, Hong S, Han B. Learning deconvolution network for semantic segmentation[C], 1520-1528(2015).

[12] Ronneberger O, Fischer P, Brox T. U-Net: convolutional networks for biomedical image segmentation[M]. Navab N, Hornegger J, Wells W M, et al. Medical image computing and computer-assisted intervention-MICCAI 2015, 9351, 234-241(2015).

[13] Paszke A, Chaurasia A, Kim S et al. ENet: a deep neural network architecture for real-time semantic segmentation[EB/OL]. https://arxiv.org/abs/1606.02147

[14] Wang Y, Zhou Q, Liu J et al. Lednet: a lightweight encoder-decoder network for real-time semantic segmentation[C], 1860-1864(2019).

[15] Zhao H S, Shi J P, Qi X J et al. Pyramid scene parsing network[C], 6230-6239(2017).

[16] Chen L C, Papandreou G, Kokkinos I et al. DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40, 834-848(2018).

[17] Zhang R, Tang S, Lin M et al. Global-residual and local-boundary refinement networks for rectifying scene parsing predictions[C], 3427-3433(2017).

[18] Chen L C, Zhu Y K, Papandreou G et al. Encoder-decoder with atrous separable convolution for semantic image segmentation[M]. Ferrari V, Hebert M, Sminchisescu C, et al. Computer vision-ECCV 2018, 11211, 801-818(2018).

[19] Woo S, Park J, Lee J Y et al. CBAM: convolutional block attention module[M]. Ferrari V, Hebert M, Sminchisescu C, et al. Computer vision-ECCV 2018, 11211, 3-19(2018).

[20] Guo L, Zhang T S, Sun W Z et al. Image semantic description algorithm with integrated spatial attention mechanism[J]. Laser & Optoelectronics Progress, 58, 1210030(2021).

[21] Everingham M, Eslami S M A, van Gool L et al. The pascal visual object classes challenge: a retrospective[J]. International Journal of Computer Vision, 111, 98-136(2015).