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Volume - 7 | Issue - 1 | march 2025

CSOD-24: Construction Site Object Detection Dataset for Safety Monitoring at Construction Site using Deep Learning Open Access
Meenakshi N. Shrigandhi  , Sachin R. Gengaje  1406
Pages: 182-206
Cite this article
Shrigandhi, Meenakshi N., and Sachin R. Gengaje. "CSOD-24: Construction Site Object Detection Dataset for Safety Monitoring at Construction Site using Deep Learning." Journal of Innovative Image Processing 7, no. 1 (2025): 182-206
Published
25 April, 2025
Abstract

Monitoring the use of personal protective equipment (PPE) and worker proximity to heavy machinery are two areas where ensuring safety compliance on construction sites continues to be difficult. The lack of dynamic ambient circumstances, comprehensive annotations, and real-time video data in existing datasets restricts their applicability to real-world situations. In order to fill in these gaps, this work presents CSOD-24, a video dataset intended for construction site object detection and safety monitoring. The dataset includes 100 ten-second video clips (16.6 minutes total), covering four major classes: "Dump Truck", "Worker with Helmet", "Worker without Helmet" and "Excavator". The videos were recorded at 10 frames per second (fps) and annotated in .txt, .json, and .xml formats. This dataset supports the development and validation of algorithms for automated safety compliance monitoring, object detection, and tracking in dynamic construction environments. The CSOD-24 dataset address these challenges, enabling a robust foundation for advancing computer vision-based safety monitoring, thereby contributing to reduced workplace hazards and improved operational efficiency.

Keywords

Safety Monitoring Construction Equipment Personal Protective Equipment Object Detection Classification Proximity Detection

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