Abstract

This work offers an extensive performance evaluation of video-based pothole detection algorithms utilizing a unique dataset of 619 high-resolution movies recorded in South Kalimantan, Indonesia. Seven distinct models were assessed: three multi-frame-based methodologies (Best Frame Selection, Temporal Consistency Loss, and Multi-Frame Ensemble) employing U-Net architectures with temporal modeling, three per-frame models (OneFormer, YOLOv8-seg, and YOLACT), and one fusion ensemble integrating the per-frame models via weighted boxes fusion. The video collection consists of 2 s segments containing 48 frames each, accompanied by ground truth segmentation masks for pothole identification. Results indicate that per-frame models substantially surpass video-based methods, with the fusion ensemble attaining 81% IoU, followed by YOLOv8-seg and OneFormer, each getting 80% IoU. Parameter efficiency investigation indicates that YOLOv8-seg is the most efficient, achieving IoU per million parameters.

IPC Classification

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