ECSNet: An Accelerated Real-Time Image Segmentation CNN Architecture for Pavement Crack Detection

The ability to perform pixel-wise segmentation on pavement cracks in real-time is paramount in road service condition assessment and maintenance decision-making practices. Recent deep learning detection models are focused on detection accuracy and require a large number of computing sources and long run times. However, highly efficient and accelerated models with acceptable accuracy in real-time pavement crack detection tasks are required but hard to achieve. In this work, we present a customized deep learning model architecture named Efficient Crack Segmentation Neural Network (ECSNet) for accelerated real-time pavement crack detection and segmentation without compromising performance. We introduce some novel parts, including small kernel convolutional layers and parallel max pooling and convolutional operation, into the architecture for crack information quickly extraction and model's parameter reduction. We test latency and accuracy trade-offs of our proposed model using the DeepCrack Dataset. The results demonstrate strong performance in both accuracy and efficiency compared to other state-of-the-art models including DeepLabV3, FCN, LRASPP, Enet, Unet and DeepCrack. It is promising that ECSNet obtains the second place with an F1 score of (84.45%) and an Intersection over Union (IoU) of 73.08%. Furthermore, our model gains the largest Frames Per Second (FPS) and lowest training time among all the models which is 73.29 and 5011 seconds, respectively. It maintains a good balance between accuracy and efficiency metrics.