A Systematic Accuracy–Efficiency Evaluation of YOLO11 for Fine-Grained Vehicle Detection in Urban CCTV Traffic
Shashank Mudgal
Shashank Mudgal, Department of Electronics and Communication Engineering, Roorkee Institute of Technology, India.
Manuscript received on 14 August 2026 | First Revised Manuscript received on 19 August 2026 | Second Revised Manuscript received on 02 September 2026 | Manuscript Accepted on 15 September 2026 | Manuscript published on 30 September 2026 | PP: 1-3 | Volume-13 Issue-9, September 2026 | Retrieval Number: 100.1/ijies.I115113090926 | DOI: 10.35940/ijies.I1151.13090926
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Fine-grained vehicle detection in urban CCTV imagery requires balancing recognition quality and inference efficiency. This paper presents a systematic empirical evaluation of YOLO11 on the BMD-45 urban traffic dataset using the provided experimental results. The study evaluates model scale, input resolution, augmentation configuration, hardware platform, and class-wise performance. For YOLO11n at 640×640, the reported precision, recall, mAP@0.50, and mAP@0.50:0.95 are 91.40%, 87.20%, 93.10%, and 68.20%, respectively, with a mean latency of 18.7 ms and 53.48 FPS on the reported T4 configuration. Scaling from YOLO11n to YOLO11l increases mAP@0.50:0.95 by 5.40 percentage points but increases latency from 18.7 to 48.6 ms. Increasing resolution from 416×416 to 768×768 increases mAP@0.50:0.95 by 5.30 percentage points while latency rises from 11.8 to 27.9 ms. The supplied hardware results range from 77.52 FPS on an L4 configuration to 5.43 FPS on the reported CPU baseline. Under the supplied conditions, the analysis identifies YOLO11n at 640×640 as a practical low-latency operating point.
Keywords: YOLO11; BMD-45; Vehicle Detection; CCTV; Intelligent Transportation Systems; Edge AI; Real-Time Object Detection; Accuracy–Efficiency Trade-off.
Scope of the Article: Computer Science and Engineering