Work place: Karabuk University / Computer Engineering, Karabuk, Turkey
Research Interests: Real-Time Computing, Solid Modeling, Intrusion Detection System
Mohammed Abduljabbar Zaid received B.Sc. in Computer Engineering from Al Mamoun University College, Baghdad, Iraq in 2014; He received his MSc. degree in Computer Engineering in 2023 from Karabuk University, Karabuk, Turkey. His research interest includes Object Detection. His professional activities have been focused on Real-Time Object Detection System, Vehicle Counting Model.
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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