Advanced Driving Assistance Integration in Electric Motorcycles: Road Surface Classification with a Focus on Gravel Detection Using Deep Learning

Ranan Venancio ¹ Vítor Manuel De Filipe ^1,2 Adelaide da Cruz Cerveira ^1,2 Lio Gonçalves ^1,2*

• ¹ University of Trás-os-Montes and Alto Douro, Vila Real, Portugal
• ² Institute for Systems and Computer Engineering, Technology and Science, Faculty of Engineering, University of Porto, Lisbon, Porto, Portugal

Riding a motorcycle involves risks that can be minimized through advanced sensing and response systems to assist the rider. The use of camera-collected images to monitor road conditions can aid in the development of tools designed to enhance rider safety and prevent accidents. This paper proposes a method for developing deep learning models designed to operate efficiently on embedded systems like the Raspberry Pi, facilitating real-time decisions that consider the road condition.Our research tests and compares several state-of-the-art convolutional neural network architectures, including EfficientNet and Inception, to determine which offers the best balance between inference time and accuracy. Specifically, we measured top-1 accuracy and inference time on a Raspberry Pi, identifying EfficientNetV2 as the most suitable model due to its optimal trade-off between performance and computational demand. The model's top-1 accuracy significantly outperformed other models while maintaining competitive inference speeds, making it ideal for real-time applications in traffic-dense urban settings.