Real-Time Vehicle Detection for Surveillance of River Dredging Areas Using Convolutional Neural Networks
Küçük Resim Yok
Tarih
2023
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Modern Education and Computer Science Press
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
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. © 2023, Modern Education and Computer Science Press. All rights reserved.
Açıklama
Anahtar Kelimeler
Automated surveillance, CNN, River dredging, Scale invariant, Vehicle detection
Kaynak
International Journal of Image, Graphics and Signal Processing
WoS Q Değeri
Scopus Q Değeri
N/A
Cilt
15
Sayı
5
