Container damage detection using advanced computer vision model Yolov12 vs Yolov11 vs RF-DETR A comparative analysis
This work addresses safety and liability issues in the logistics industry by improving timely inspection of damaged containers, but it is incremental as it compares existing models without introducing new methods.
This paper tackled the problem of container damage detection by comparing three computer vision models (Yolov12, Yolov11, and RF-DETR) on a dataset of 278 annotated images, finding that Yolov11 and Yolov12 achieved a higher mAP@50 score of 81.9% compared to RF-DETR's 77.7%, but RF-DETR performed better on not-so-common damaged containers.
Containers are an integral part of the logistics industry and act as a barrier for cargo. A typical service life for a container is more than 20 years. However, overtime containers suffer various types of damage due to the mechanical as well as natural factors. A damaged container is a safety hazard for the employees handling it and a liability for the logistic company. Therefore, a timely inspection and detection of the damaged container is a key for prolonging service life as well as avoiding safety hazards. In this paper, we will compare the performance of the damage detection by three state-of-the-art advanced computer vision models Yolov12, Yolov11 and RF-DETR. We will use a dataset of 278 annotated images to train, validate and test the model. We will compare the mAP and precision of the model. The objective of this paper is to identify the model that is best suited for container damage detection. The result is mixed. mAP@50 score of Yolov11 and 12 was 81.9% compared to RF-DETR, which was 77.7%. However, while testing the model for not-so-common damaged containers, the RF-DETR model outperformed the others overall, exhibiting superiority to accurately detecting both damaged containers as well as damage occurrences with high confidence.