Detection of Pavement Defects on Roads using a Multimodal YOLOv8 with Image and IMU Data

Monitoring and maintaining the conditions of roads is a challenging and essential task. Object detection models can accelerate the assessment of roads, making it more efficient and standardized. Recent studies have explored automating road assessment using images and inertial sensors, such as accelerometers. This work hypothesizes that a multimodal model, combining image and sensor data, improves the performance of pavement defect detection. Experimental results show that the proposed YOLOv8-MultiDetect (multimodal) model outperforms the YOLOv8-Detect (unimodal) model, with average [email protected] scores of 0.809 versus 0.759 and [email protected]:0.65 scores of 0.780 versus 0.726. These findings suggest that integrating features from multiple data sources enhances both object classification and localization in detecting road defects.