Abu Awwad Yaser, Rana Omer, Perera Charith
Department of Computer Science and Informatics, Cardiff University, Cardiff CF24 4AG, UK.
Sensors (Basel). 2024 Jan 24;24(3):772. doi: 10.3390/s24030772.
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.
在智慧城市领域中，用于监控户外城乡区域（如农场和森林）以防止农业机械和牲畜被盗以及监控工人以保障其安全的摄像头数量日益显著。然而，在低光照条件的环境中，异常检测任务变得更具挑战性。因此，要在识别周围行为和事件方面取得高效成果变得困难。所以，本研究开发了一种技术来增强在能见度差的情况下拍摄的图像。这种增强旨在提高目标检测精度并减少误报检测。所提出的技术包括几个阶段。在第一阶段，从输入图像中提取特征。随后，一个分类器分配一个唯一标签以指示多增强网络中的最优模型。此外，它可以区分充足光照下拍摄的场景和低光照场景。最后，应用一种检测算法来识别目标。每个任务都在一个单独的物联网边缘设备上实现，在ExDark数据库上提高了检测性能，所有阶段的响应时间接近一秒。
