Work place: Karabuk University / Electrical and Electronics Engineering, Karabuk, Turkey
Research Interests: Computer Networks, Security Services
Muhammet Çakmak received his MSc. and PhD degree in Computer Engineering, Karabuk University, Karabuk, Turkey. He is currently working as an Assistant Professor at Karabuk University Electrical and Electronic Engineering. His research interest includes Computer Networks, Cyber Security, and Deep Learning.
DOI: https://doi.org/10.5815/ijigsp.2023.05.02, Pub. Date: 8 Oct. 2023
The presence of illegal activities such as illegitimate mining and sand theft in river dredging areas leads to economic losses. However, manual monitoring is expensive and time-consuming. Therefore, automated surveillance systems are preferred to mitigate such activities, as they are accurate and available at all times. In order to monitor river dredging areas, two essential steps for surveillance are vehicle detection and license plate recognition. Most current frameworks for vehicle detection employ plain feed-forward Convolutional Neural Networks (CNNs) as backbone architectures. However, these are scale-sensitive and cannot handle variations in vehicles' scales in consecutive video frames. To address these issues, Scale Invariant Hybrid Convolutional Neural Network (SIH-CNN) architecture is proposed for real-time vehicle detection in this study. The publicly available benchmark UA-DETRAC is used to validate the performance of the proposed architecture. Results show that the proposed SIH-CNN model achieved a mean average precision (mAP) of 77.76% on the UA-DETRAC benchmark, which is 3.94% higher than the baseline detector with real-time performance of 48.4 frames per seconds.[...] Read more.
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