Resumen
Due to the various sizes of each object, such as kilometer stones, detection is still a challenge, and it directly impacts the accuracy of these object counts. Transformers have demonstrated impressive results in various natural language processing (NLP) and image processing tasks due to long-range modeling dependencies. This paper aims to propose an exceeding you only look once (YOLO) series with two contributions: (i) We propose to employ a pre-training objective to gain the original visual tokens based on the image patches on road asset images. By utilizing pre-training Vision Transformer (ViT) as a backbone, we immediately fine-tune the model weights on downstream tasks by joining task layers upon the pre-trained encoder. (ii) We apply Feature Pyramid Network (FPN) decoder designs to our deep learning network to learn the importance of different input features instead of simply summing up or concatenating, which may cause feature mismatch and performance degradation. Conclusively, our proposed method (Transformer-Based YOLOX with FPN) learns very general representations of objects. It significantly outperforms other state-of-the-art (SOTA) detectors, including YOLOv5S, YOLOv5M, and YOLOv5L. We boosted it to 61.5% AP on the Thailand highway corpus, surpassing the current best practice (YOLOv5L) by 2.56% AP for the test-dev data set.