Onboard LiDAR–Camera Deployment Optimization for Pavement Marking Distress Fusion Detection

• Published in: Sensors (Basel, Switzerland) Vol. 25; no. 13; p. 3875
• Main Authors: Lin, Ciyun, Sun, Wenjian, Sun, Ganghao, Gong, Bown, Liu, Hongchao
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 21.06.2025; MDPI
• Subjects: Accuracy; Algorithms; Cameras; Lasers; Linear programming; Methods; multi-sensor data fusion; Optical radar; Optimization; pavement marking distress detection; red fox optimization; sensor deployment optimization; Sensors; China; multi-sensor data fusion; sensor deployment optimization; pavement marking distress detection; red fox optimization
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s25133875

Pavement markings, as a crucial component of traffic guidance and safety facilities, are subject to degradation and abrasion after a period of service. To ensure traffic safety, retroreflectivity and diffuse illumination should be above the minimum thresholds and required to undergo inspection periodically. Therefore, an onboard light detection and ranging (LiDAR) and camera deployment optimization method is proposed for pavement marking distress detection to adapt to complex traffic conditions, such as shadows and changing light. First, LiDAR and camera sensors’ detection capability was assessed based on the sensors’ built-in features. Then, the LiDAR–camera deployment problem was mathematically formulated for pavement marking distress fusion detection. Finally, an improved red fox optimization (RFO) algorithm was developed to solve the deployment optimization problem by incorporating a multi-dimensional trap mechanism and an improved prey position update strategy. The experimental results illustrate that the proposed method achieves 5217 LiDAR points, which fall on a 0.58 m pavement marking per data frame for distress fusion detection, with a relative error of less than 7% between the mathematical calculation and the field test measurements. This empirical accuracy underscores the proposed method’s robustness in real-world scenarios, effectively mitigating the challenges posed by environmental interference.