Combining Image Processing and Machine Learning for Object Detection in Complex Environments

The goal of object detection research is to address safety issues at industrial locations; yet, optimizing object detection efficiency while maintaining real-time capabilities is still difficult in complicated industrial settings. An unconditionally protected detector is proposed in this research to solve this issue. A bottleneck-structured residual block is used by the detector's backbone network to improve real-time performance and minimize computation. Additionally, a selective attentiveness network enhances object detection by recognizing important characteristics based on the neck structure's feature map's morphological attributes. The updated YOLOv4 model, Faster RCNN, and SSD on the COCO and KITTI datasets for object identification, is proposed in this research to address the aforementioned issues. Particularly important tasks requiring strong mathematical underpinnings are picture segmentation, object detection, and classification. Although there have been improvements, there are still issues, like the need to define terms like deep learning, machine learning, and artificial intelligence. A strong research foundation requires precise definitions and interpretations. The development of machine vision is a reflection of a bold attempt to mimic human eyesight. The ability to simulate human behaviour and perception has advanced remarkably thanks to interdisciplinary cooperation and using machine learning techniques.