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We present a monocular-vision pipeline for estimating the volume (m3) of travertine-marble blocks transported on trucks, using only RGB footage from fixed surveillance cameras. Unlike truck-scale systems that are costly and provide only aggregate weights, our approach estimates dimensions per block. A YOLOv8-Pose model trained on 598 annotated frames achieved \({mAP}_{0.5}\) = 95.9% for bounding boxes and \({mAP}_{0.5:0.95}\) = 95.97% for five keypoints. Metric conversion combines intrinsic calibration with a planar homography anchored to the truck platform, while block height is inferred from a single reference block. On 31 test frames, the system reached a mean absolute error of 0.97 m3 (≈14% relative, typical block ≈ 7 m3). For comparison, we evaluated a CNN regressor (ResNet18), tabular models (Ridge, Random Forest), and a MiDaS-based pseudo-depth height estimator. These alternatives showed higher errors (RF 16.4%, ResNet18 25.7%, Ridge 38.8%, MiDaS 18.4%), confirming the proposed homography-based method as the most accurate under quarry conditions.

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Volume Estimation of Travertine Blocks Using Keypoint Detection and Homography from Monocular Video

  • 会议论文

  • 发表日期：

    2026年

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  • Erik Vargas Arostegui,
  • Lourdes Ramirez Cerna

摘要

We present a monocular-vision pipeline for estimating the volume (m3) of travertine-marble blocks transported on trucks, using only RGB footage from fixed surveillance cameras. Unlike truck-scale systems that are costly and provide only aggregate weights, our approach estimates dimensions per block. A YOLOv8-Pose model trained on 598 annotated frames achieved \({mAP}_{0.5}\) = 95.9% for bounding boxes and \({mAP}_{0.5:0.95}\) = 95.97% for five keypoints. Metric conversion combines intrinsic calibration with a planar homography anchored to the truck platform, while block height is inferred from a single reference block. On 31 test frames, the system reached a mean absolute error of 0.97 m3 (≈14% relative, typical block ≈ 7 m3). For comparison, we evaluated a CNN regressor (ResNet18), tabular models (Ridge, Random Forest), and a MiDaS-based pseudo-depth height estimator. These alternatives showed higher errors (RF 16.4%, ResNet18 25.7%, Ridge 38.8%, MiDaS 18.4%), confirming the proposed homography-based method as the most accurate under quarry conditions.