Detecting surface cracks on buildings using computer vision: an experimental comparison of digital image processing and deep learning
Conference: Soft Computing and Signal Processing (pp. 197-210). Springer, Singapore.
Abstract: The construction of a building involves tremendous investments of time, money, and emotion. Therefore, every stakeholder involved in the process starting from construction companies to the tenants wants to make sure that a structure is built well and that it can serve its purpose without any safety hazards. While the majority of factors concerning a building’s safety are evaluated manually, there are factors like detecting visible structural damage that might incur a severe investment of time via manual inspection. Therefore, the need of the hour is to engineer automated systems that with the help of computer vision techniques will detect visually discernible defects in buildings. The paper proposes two approaches, namely digital image processing-based and deep learning-based that deal with creating surface crack inspection systems and attempt to showcase their performances in perspective by comparing their results across four different types of surface crack image datasets.
Identification of Criminal Activity Hotspots using Machine Learning to Aid in Effective Utilization of Police Patrolling in Cities with High Crime Rates
Conference: 4th International Conference on Computational Systems and Information Technology for Sustainable Solution (CSITSS) (Vol. 4, pp. 1-6). IEEE.
Abstract: Criminal activity has always been a major deterrent in human progress and the constant presence of criminal activities stemming out of multifarious causes has been a hindrance for human sustainable living. The problem further aggravates when there is a dearth of police force to prevent crime. In countries like India where the police to population ratio is much less than the United Nations’ Standard, the need of the hour is to efficiently utilize the existing force to prevent crimes. In this paper, we propose a solution that would facilitate effective distribution of police forces in a city among multiple districts based on the extent to which each district is prone to crime at a given hour, in a given day, for a given month. We have used the Chicago Crime Dataset in this work. The problem has been modelled as an imbalanced classification problem and supervised machine learning algorithms such as Logistic Regression, Naive Bayes, K Nearest Neighbours, Support Vector Machines, Decision Trees, Random Forests, Gradient Boosting Trees have been employed and their performances have been evaluated. In particular, the Gradient Boosting Tree has achieved the best performance in our case.