Haoyue Wang
ABSTRACT
Observing X-ray images manually is a common method for detecting contraband in packages. Long-term continuous observation is prone to visual fatigue, leading to problems of missed detection and false detection. Motivated by aiding operators in contraband detection in packages, we propose X-YOLO which is a deep learning based toolset with multiple optimization strategies for contraband detection to increase the detection precision. The path enhancement is designed to shorten the information path between the lower layer and the uppermost layer. We replace Leaky ReLU with Swish and Steps with SGDR to make training stable. Mixup, a dataset-independent method for data augmentation, is designed to increase the amount of training data for improving the generalization of model without expert knowledge. In order to solve the issue that Intersection over Union (IoU) can not deal with two non-overlapping objects, we apply Generalized Intersection over Union (GIoU) as bounding box losses. The experimental results show that X-YOLO achieves mAP up to 96.02% and recall up to 98.55%, surpassing Faster R-CNN, SSD, YOLOv1, YOLOv2, Tiny-YOLO, YOLOv3, YOLOv3-tiny, YOLOv3-spp and YOLOv3 with some of optimization strategies.
- A. D. Pitcher, J. J. McCombe, E. A. Eveleigh, and N. K. Nikolova. Compact transmitter for pulsed-radar detection of on-body concealed weapons. In 2018 IEEE/MTT-S International Microwave Symposium - IMS, pages 919--922, June 2018.Google Scholar
Cross Ref
- K. He, X. Zhang, S. Ren, and J. Sun. Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37(9): 1904--1916, Sep. 2015.Google ScholarDigital Library
- R. R. Thirrunavukkarasu, T. Meeradevi, A. Ravi, D. Ganesan, and G. P. Vadivel. Detection r peak in electrocardiogram signal using daubechies wavelet transform and shannon's energy envelope. In 2019 5th International Conference on Advanced Computing Communication Systems (ICACCS), pages 1044--1048, March 2019.Google ScholarCross Ref
- D. Mery and A. K. Katsaggelos. A logarithmic x-ray imaging model for baggage inspection: Simulation and object detection. In 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pages 251--259, July 2017.Google ScholarCross Ref
- Muhammet Baundefinedtan. Multi-view object detection in dual-energy x-ray images. Mach. Vision Appl., 26(7-8): 1045--1060, November 2015.Google ScholarDigital Library
- V. Riffo and D. Mery. Automated detection of threat objects using adapted implicit shape model. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 46(4): 472--482, April 2016.Google ScholarCross Ref
- D. Mery, E. Svec, M. Arias, V. Riffo, J. M. Saavedra, and S. Banerjee. Modern computer vision techniques for x-ray testing in baggage inspection. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 47(4): 682--692, April 2017.Google ScholarCross Ref
- M. E. Kundegorski, S. Akcay, M. Devereux, A. Mouton, and T. P. Breckon. On using feature descriptors as visual words for object detection within x-ray baggage security screening. In 7th International Conference on Imaging for Crime Detection and Prevention (ICDP 2016), pages 1--6, Nov 2016.Google ScholarCross Ref
- S. Akçay, M. E. Kundegorski, M. Devereux, and T. P. Breckon. Transfer learning using convolutional neural networks for object classification within x-ray baggage security imagery. In 2016 IEEE International Conference on Image Processing (ICIP), pages 1057--1061, Sep. 2016.Google ScholarCross Ref
- Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2016.Google ScholarCross Ref
- S. Akcay, M. E. Kundegorski, C. G. Willcocks, and T. P. Breckon. Using deep convolutional neural network architectures for object classification and detection within x-ray baggage security imagery. IEEE Transactions on Information Forensics and Security, 13(9): 2203--2215, Sep. 2018.Google ScholarCross Ref
- Dengsheng Zhang. Support Vector Machine, pages 179--205. Springer International Publishing, Cham, 2019.Google Scholar
- Yuanxi Wei and Xiaoping Liu. Dangerous goods detection based on transfer learning in x-ray images. Neural Computing and Applications, Jul 2019.Google ScholarDigital Library
- Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. Faster r-cnn: Towards real-time object detection with region proposal networks. In C. Cortes, N. D. Lawrence, D. D. Lee, M. Sugiyama, and R. Garnett, editors, Advances in Neural Information Processing Systems 28, pages 91--99. Curran Associates, Inc., 2015.Google Scholar
- Joseph Redmon, Santosh Divvala, Ross Girshick, and Ali Farhadi. You only look once: Unified, real-time object detection. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2016.Google ScholarCross Ref
- Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, and Alexander C. Berg. Ssd: Single shot multibox detector. In Bastian Leibe, Jiri Matas, Nicu Sebe, and Max Welling, editors, Computer Vision - ECCV 2016, pages 21--37, Cham, 2016. Springer International Publishing.Google ScholarCross Ref
- Ilya Loshchilov and Frank Hutter. SGDR: stochastic gradient descent with restarts. CoRR, abs/1608.03983, 2016.Google Scholar
- Prajit Ramachandran, Barret Zoph, and Quoc V. Le. Searching for activation functions. CoRR, abs/1710.05941, 2017.Google Scholar
- Seyed Hamid Rezatofighi, Nathan Tsoi, JunYoung Gwak, Amir Sadeghian, Ian D. Reid, and Silvio Savarese. Generalized intersection over union: A metric and A loss for bounding box regression. CoRR, abs/1902.09630, 2019.Google Scholar
- Hongyi Zhang, Moustapha Cissé, Yann N. Dauphin, and David Lopez-Paz. mixup: Beyond empirical risk minimization. CoRR, abs/1710.09412, 2017.Google Scholar
- Mohammad Mahdi Derakhshani, Saeed Masoudnia, Amir Hossein Shaker, Omid Mersa, Mohammad Amin Sadeghi, Mohammad Rastegari, and Babak Nadjar Araabi. Assisted excitation of activations: A learning technique to improve object detectors. CoRR, abs/1906.05388, 2019.Google Scholar
- Alex Graves, Marc G. Bellemare, Jacob Menick, Rémi Munos, and Koray Kavukcuoglu. Automated curriculum learning for neural networks. In Doina Pre-cup and Yee Whye Teh, editors, Proceedings of the 34th International Conference on Machine Learning, volume 70 of Proceedings of Machine Learning Research, pages 1311--1320, International Convention Centre, Sydney, Australia, 06--11 Aug 2017. PMLR.Google ScholarDigital Library
- Shu Liu, Lu Qi, Haifang Qin, Jianping Shi, and Jiaya Jia. Path aggregation network for instance segmentation. CoRR, abs/1803.01534, 2018.Google Scholar
- Adam Marrs, Josef Spjut, Holger Gruen, Rahul Sathe, and Morgan McGuire. Adaptive temporal antialiasing. In Proceedings of the Conference on High-Performance Graphics, HPG '18, New York, NY, USA, 2018. Association for Computing Machinery.Google ScholarDigital Library
Index Terms
- X-YOLO: A deep learning based toolset with multiple optimization strategies for contraband detection
Recommendations
YOLO-FA: Type-1 fuzzy attention based YOLO detector for vehicle detection
AbstractVehicle detection is an important component of intelligent transportation systems and autonomous driving. However, in real-world vehicle detection scenarios, the presence of many complex and high uncertainty factors, such as illumination ...
Highlights- A type-1 fuzzy attention (T1FA) mechanism-based YOLO (YOLO-FA) detector is proposed.
- Mixed depthwise convolution is employed as a token mixer in MetaFormer.
- T1FA can boost 3.2% AP50 in UA-DETRAC by reducing the uncertainty.
- ...
YOLO-face: a real-time face detector
AbstractFace detection is one of the important tasks of object detection. Typically detection is the first stage of pattern recognition and identity authentication. In recent years, deep learning-based algorithms in object detection have grown rapidly. ...
TRC‐YOLO: A real‐time detection method for lightweight targets based on mobile devices
AbstractObject detection is one of the main tasks of computer vision. Object detection algorithms usually rely on deep convolutional neural networks, which require the host device to have high computing capabilities, greatly limiting the application of ...
Comments