Anomaly detection on the edge using smart cameras under low-light conditions

Abu Awwad, Yaser, Rana, Omer ORCID: https://orcid.org/0000-0003-3597-2646 and Perera, Charith ORCID: https://orcid.org/0000-0002-0190-3346 2024. Anomaly detection on the edge using smart cameras under low-light conditions. Sensors 24 (3) , 772. 10.3390/s24030772

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Abstract

The number of cameras utilised in smart city domains is increasingly prominent and notable for monitoring outdoor urban and rural areas such as farms and forests to deter thefts of farming machinery and livestock, as well as monitoring workers to guarantee their safety. However, anomaly detection tasks become much more challenging in environments with low-light conditions. Consequently, achieving efficient outcomes in recognising surrounding behaviours and events becomes difficult. Therefore, this research has developed a technique to enhance images captured in poor visibility. This enhancement aims to boost object detection accuracy and mitigate false positive detections. The proposed technique consists of several stages. In the first stage, features are extracted from input images. Subsequently, a classifier assigns a unique label to indicate the optimum model among multi-enhancement networks. In addition, it can distinguish scenes captured with sufficient light from low-light ones. Finally, a detection algorithm is applied to identify objects. Each task was implemented on a separate IoT-edge device, improving detection performance on the ExDark database with a nearly one-second response time across all stages.

Item Type:	Article
Date Type:	Published Online
Status:	Published
Schools:	Computer Science & Informatics
Publisher:	MDPI
ISSN:	1424-8220
Funders:	DCMS
Date of First Compliant Deposit:	26 January 2024
Date of Acceptance:	17 January 2024
Last Modified:	28 Mar 2024 14:30
URI:	https://orca.cardiff.ac.uk/id/eprint/165873

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