Current Volume 9
Efficient Net-B3 with Convolutional Block Attention and Focal Loss for Road Accident Detection in CCTV Surveillance Videos
Abstract
Automated road accident detection from CCTV footage is a critical public safety challenge in smart city environments. Prior work using finetuned AlexNet on the ckay16 dataset achieved only 68.0% accuracy, a true positive rate (TPR) of 77.4%, and a critically high false positive rate (FPR) of 42.6% — rendering the system impractical for live deployment. In this paper we present a fully algorithmic framework that addresses these shortcomings without collecting additional data. Our method replaces AlexNet with an EfficientNet-B3 backbone, attaches a Convolutional Block Attention Module (CBAM) to the final feature block, and trains using Focal Loss with label smoothing, a Weighted Random Sampler, and a cosine-annealing learning-rate schedule with warm restarts. At inference, five-crop Test-Time Augmentation (TTA) further reduces false positives. On the held-out ckay16 test set the system achieves 96.0% accuracy, 95.7% TPR, 3.8% FPR, macro-F1 of 96.0%, and ROC-AUC of 0.981 — a 28-point accuracy gain and 39-point FPR reduction over the AlexNet baseline, demonstrating significant improvement over existing baselines.
Keywords
Road accident detection, EfficientNet, CBAM, Attention mechanism, Focal loss, Test-time augmentation, Transfer learning, CCTV surveillance
Citations
IRE Journals:
M Koteswara Rao, G Raja Sekhar Reddy, P Swarna Kamal, P S C S V Sainadh "Efficient Net-B3 with Convolutional Block Attention and Focal Loss for Road Accident Detection in CCTV Surveillance Videos" Iconic Research And Engineering Journals Volume 9 Issue 10 2026 Page 3725-3731 https://doi.org/10.64388/IREV9I10-1717084
IEEE:
M Koteswara Rao, G Raja Sekhar Reddy, P Swarna Kamal, P S C S V Sainadh
"Efficient Net-B3 with Convolutional Block Attention and Focal Loss for Road Accident Detection in CCTV Surveillance Videos" Iconic Research And Engineering Journals, 9(10) https://doi.org/10.64388/IREV9I10-1717084
About IRE Journals
IRE Journals is an open access online journals established with an aim to publish high quality of research work in various diciplines. We have more than 6 years in publications. Over 2500+ papers already published.
Important Links
For Authors
Volume 8:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 7:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 6:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 5:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 4:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 3:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12