A Deep Assessment of Thermal Image-Base Object Detection for a Wide Range of Applications

Computer vision enables for the detection of things even in densely populated areas. This work investigates autonomous object detection and activity recognition in static and dynamic images, as well as their potential and restrictions. Several object identification challenges are addressed in relation to illness diagnosis, identifying wild animals, and identifying industrial activities, such as processing, segmentation, and extraction techniques for various object features. It is also critical to highlight the limitations, gaps, technical problems, applicability, and state-of-the-art of the various techniques. Shape-based detection, skin color extraction, edge identification, and an object detector are all approaches we use to identify and classify objects. This contrasts with motion detection, which is less precise when utilized at night. Thermal imaging has been used to track objects in a variety of applications, including surveillance systems and the identification of people and wildlife. We plan to evaluate new object detectors such as YOLO and RCNN. Another route for future research was suggested: investigate various object identification systems, particularly those that deal with thermal video or image detection.