Improved YOLOv5-Based Real-Time Road Pavement Damage Detection in Road Infrastructure Management

Abstract

Deep learning has enabled a straightforward, convenient method of road pavement infrastructure management that facilitates a secure, cost-effective, and efficient transportation network. Manual road pavement inspection is time-consuming and dangerous, making timely road repair difficult. This research showcases You Only Look Once version 5 (YOLOv5), the most commonly employed object detection model trained on the latest benchmark Road Damage Dataset, Road Damage Detection 2022 (RDD 2022). The RDD 2022 dataset includes four common types of road pavement damage, namely vertical cracks, horizontal cracks, alligator cracks, and potholes. This paper presents an improved deep neural network model based on YOLOv5 for real-time road pavement damage detection in photographic representations of outdoor road surfaces, making it an indispensable tool for efficient, real-time, and cost-effective road infrastructure management. The YOLOv5 model has been modified to incorporate several techniques that improve its accuracy and generalization performance. These techniques include the Efficient Channel Attention module (ECA-Net), label smoothing, the K-means++ algorithm, Focal Loss, and an additional prediction layer. In addition, a 1.9% improvement in mean average precision (mAP) and a 1.29% increase in F1-Score were attained by the model in comparison to YOLOv5s, with an increment of 1.1 million parameters. Moreover, a 0.11% improvement in mAP and 0.05% improvement in F1 score was achieved by the proposed model compared to YOLOv8s while having 3 million fewer parameters and 12 gigabytes fewer Giga Floating Point Operation per Second (GFlops).

A Pavement Crack Detection and Evaluation Framework for a UAV Inspection System Based on Deep Learning

“…To validate the reliability and advantages of our self-made crack datasets, we conducted a comparative study using these four model algorithms on existing various open-source UAV pavement crack datasets. Our experiment involved comparing the detection accuracy of our crack datasets with datasets such as UAPD [2], RDD2022 [31], UMSC [19], UAVRoadCrack [21], and CrackForest [32]. We evaluated and compared the accuracy performances of Faster-RCNN, YOLOv5, YOLOv7-tiny, and YOLOv8s after 200 training cycles, as well as Faster-RCNN after 15 rounds.…”

A Pavement Crack Detection and Evaluation Framework of UAV inspection System Based on Deep Learning

“…To validate the reliability and advantages of our self-made cracks datasets, we conducted a comparative study using these four model algorithms on existing various open-source UAV pavement cracks datasets. Our experiment involved comparing the detection accuracy of our cracks datasets with datasets such as UAPD [2], RDD2022 [30], UMSC[19], UAVRoadCrack [21] and CrackForest [31]. We evaluated and compared the accuracy performance of Faster-RCNN, YOLOv5, YOLOv7-tiny, and YOLOv8s after 200 training cycles, as well as Faster-RCNN after 15 rounds.…”

OrthoDETR: A Streamlined Transformer-Based Approach for Precision Detection of Orthopedic Medical Devices

“…Carballo and team [20] introduced a new method based on computer vision and object detection technologies, utilizing the Convolutional Neural Network EfficientDet-D2 model, for cloud detection in image sequences. Sami and others [21] proposed an improved deep neural network model, based on YOLOv5, for real-time detection of road surface damage in photographic representations of outdoor road surfaces.…”