Smart IoT-Embedded Footwear for Landmine Detection (Footwear Detector)

The main goal of this project is to create and use advanced image processing techniques for the purpose of detecting footwear. The main objective is to identify and categorize different kinds of shoes for use in retail, security, and surveillance settings. Convolutional neural networks (CNNs) in particular are used by the system to support correct identification through machine learning methods. Through data augmentation and preprocessing procedures, important issues including different footwear designs, lighting circumstances, and environmental elements are addressed. The outcomes validate the proposed system's viability for practical implementation by showing that it can identify footwear with a high degree of accuracy in a variety of settings. The research on object identification and pattern recognition in computer visionis expanding, and this work adds to that body of knowledge.