Abstract
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.
Highlights
Identifying road asset objects in Thailand highway monitoring image sequences is essential for intelligent traffic monitoring and administration of the highway
The results proved that our Transformer-Based YOLOX with Feature Pyramid Network (FPN)
Our proposed YOLOX with Vision Transformer and FPN method reaches the highest performance on Average Precision (AP) rating at 61.15% in the testing set
Summary
Identifying road asset objects in Thailand highway monitoring image sequences is essential for intelligent traffic monitoring and administration of the highway. With the widespread use of traffic surveillance cameras, an extensive library of traffic video footage has been available for examination. A more distant road surface may usually be evaluated from an eye-observing angle. At this viewing angle, the vehicle’s object size varies enormously, and the detection accuracy of a small item far away from the road is low. In the face of complicated camera scenarios, it’s critical to address and implement the difficulties listed above successfully. This study applies the object detection findings for multi-object tracking and asset object counting, including kilometer signs (marked as KM Sign) and kilometer stones (marked as KM Stone)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
