Khalid, S., Ahmed, Z., Sedik, A. and Asif, Waqar ORCID: https://orcid.org/0000-0001-6774-3050 (2024) A Novel Deep Learning Framework for Contraband Items Recognition in Smart City Applications. In: 2nd International Conference on Sustainability: Developments and Innovations, ICSDI 2024, 18-22 Feb 2024, Riyadh, Saudi Arabia.

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Abstract

Ensuring safety and security is paramount in today's complex environment, and the effective detection of contraband items plays a pivotal role in achieving this objective. Contraband items, ranging from illegal substances to unauthorized goods, pose a threat to public safety, security, and the overall well-being of smart city inhabitants. Such items are currently detected by human operator reviewing the images from X-ray baggage scanners. However, manual detection of contraband items is inherently challenging and time-consuming resulting in significant delays at crowded places such as airports, train-stations, shopping malls etc. Moreover, there is a significant risk of overlooking certain items that could pose potential harm. To address these challenges, there is a growing demand for intelligent systems for contraband items detection that can efficiently and accurately detect items whilst minimizing false negatives. Automated deep learning solutions offer a sophisticated and technologically advanced approach to enhance the accuracy and speed of the detection process. In our pursuit to address this challenge comprehensively, we have obtained an X-ray Imaging Dataset specifically curated for this purpose. The dataset includes five types of objects including guns, knives, pliers, scissors, and wrenches that are typically banned to carry along. In this paper, we have proposed a deep learning-based approach to efficiently and accurately detect contraband items from X-ray images. The proposed approach is based on YOLO architectures that has been shown to perform better for object detection in variety of domains both in terms of accuracy and real-time performance. We have evaluated different versions of YOLO to select the version that works best for contraband item detection from X-ray images. Yolo-v8 has shown superior performance followed by Yolo-v5 in terms of accuracy. Challenges regarding class imbalance have been addressed using data augmentation especially for classes with limited number of samples. Comparison of proposed approach for contraband items detection with existing approaches demonstrates the superiority of proposed approach.

Item Type:	Conference or Workshop Item (Paper)
ISSN:	2366-2557
ISBN:	9789819783458
Identifier:	10.1007/978-981-97-8345-8_43
Identifier:	10.1007/978-981-97-8345-8_43
Subjects:	Computing > Intelligent systems
Related URLs:	https://link.springer.com/book/10.1007/9...
Depositing User:	Marc Forster
Date Deposited:	13 Jan 2025 09:34
Last Modified:	13 Jan 2025 09:34
URI:	https://repository.uwl.ac.uk/id/eprint/13083

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