<![CDATA[Currently, personal protective equipment (PPE) non-compliance is recorded manually through voluntary incident reports, making the data obtained less objective and prone to omissions. This situation drives the need for the application of object detection technology to improve the consistency of monitoring and the accuracy of findings related to PPE compliance. This study aims to compare three object detection architectures—Faster R-CNN, SSD, and YOLOv9—to determine the most suitable model for application in fuel oil terminal environments, particularly in red zone areas that have a high risk of atmospheric explosions. The research method includes collecting 5,000 images from incident reports and public data platforms, followed by annotation, pre-processing, and augmentation to improve model robustness. Faster R-CNN was trained with a ResNet50 and ResNet101 backbone, SSD with the same backbone, while YOLOv9 was tested with variants YOLOv9-C and YOLOv9-E. Model performance was analyzed quantitatively using mean average precision (mAP50) and loss–epoch graphs, and qualitatively through prediction visualization under real-world conditions such as small objects, visual obstructions, and low lighting. The results showed that SSD provided the most stable performance with a balanced mAP50 across training, validation, and test data, as well as consistent visual predictions across various field conditions. In conclusion, the SSD architecture is the most suitable choice for implementing PPE compliance monitoring at fuel terminals due to its acceptable accuracy and robustness to varying environmental conditions.]]>
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