Semantic Segmentation Using Lightweight DeepLabv3+ for Desiccation Crack Detection in Soil

Soil desiccation cracks in natural clayey soil pose significant risks to the stability of civil and geotechnical structures. Traditional methods for detecting these cracks are often inefficient and prone to inaccuracies. Therefore, we applied a deep learning approach of semantic segmentation based on DeepLabv3+ to detect desiccation cracks. To enhance computational efficiency, a pretrained lightweight network, MobileNetV2, was employed as the backbone for the DeepLabv3+ model. The model was trained and tested on a dataset of natural clayey soil crack images obtained through laboratory tests. Evaluation metrics including precision, recall, F1 score, and intersection over union (IoU) were used to assess the segmentation performance. The model took 17.13 min to train and achieved an inference speed of 0.43 s per image. DeepLabv3+ achieved better performance than the traditional segmentation method, with a precision of 95.76%, a recall of 84.12%, an F1 score of 89.56%, and an IoU of 81.10%. The model also demonstrated the capability to handle images with shading conditions and the presence of spots. DeepLabv3+ with MobileNetV2 as a backbone network was proven to be effective and efficient as a backbone in soil desiccation crack detection and segmentation.