AAL-Net: A Lightweight Detection Method for Road Surface Defects Based on Attention and Data Augmentation

Abstract

The pothole is a common road defect that seriously affects traffic efficiency and personal safety. Road evaluation and maintenance and automatic driving take pothole detection as their main research part. In the above scenarios, accuracy and real-time pothole detection are the most important. However, the current pothole detection methods can not meet the accuracy and real-time requirements of pothole detection due to their multiple parameters and volume. To solve these problems, we first propose a lightweight one-stage object detection network, the AAL-Net. In the network, we design an LF (lightweight feature extraction) module and use the NAM (Normalization-based Attention Module) attention module to ensure the accuracy and real time of the pothole detection process. Secondly, we make our own pothole dataset for pothole detection. Finally, in order to simulate the real road scene, we design a data augmentation method to further improve the detection accuracy and robustness of the AAL-Net. The metrics F1 and GFLOPs show that our method is better than other deep learning models in the self-made dataset and the pothole600 dataset and can well meet the accuracy and real-time requirements of pothole detection.

Bridging the Gap: Leveraging Informal Software Architecture Artifacts for Structured Model Creation

“…Our methodology can leverage AI to evolve and optimize the transformation process of architectural artifacts. This approach significantly enhances the efficiency and effectiveness of converting informal diagrams into structured, formal models by employing large language models [21] and advanced pattern recognition systems, such as those utilizing transfer learning [22] from networks like ResNet-50 [23]. This automation will speed up the process and reduce human error and the subjectivity of manual interpretation.…”