Abstract:
Effective urban management relies on timely detection and resolution of infrastructural anomalies such as sewage defects, malfunctioning traffic lights, and deformed traffic signs. Traditional methods of inspection often prove inefficient and time-consuming. In this paper, an automatic detection of the urban infrastructural issues, it presents a multi-task convolutional neural network architecture capable of simultaneously identifying sewage defects, malfunctioning traffic lights, and deformed traffic signs from street-level imagery. The model is trained on a diverse dataset comprising annotated images of urban scenes captured under various environmental conditions. We demonstrate the effectiveness of our approach through extensive experimentation and evaluation on real-world datasets. Results indicate that the model achieves high accuracy and robustness in detecting the specified anomalies, outperforming existing methods. Furthermore, we discuss the potential implications of our research for urban management, including improved efficiency in maintenance operations, enhanced safety for commuters, and cost savings for municipal authorities. About 2438 images were collected of 6 categories and were augmented twice. The first augmentation increased by (X9) for by generating data from Keras. The second augmentation was carried out on training data only by (X3) using the Roboflow tool, where we defined the angles of the shape and gave it a class name. An overall accuracy of 86% based on F1-Measure value for all classes while individual classes shows different F1-value based on the available training samples. Overall, this research contributes to the advancement of automated infrastructure inspection systems, facilitating smarter and more sustainable urban environments.
