Abstract: Object detection is one of the most basic and central task in computer vision. Its task is to find all the interested objects in the image, and determine the category and location of the objects. Object detection is widely used and has strong practical value and research prospects. Applications include face detection, pedestrian detection and vehicle detection. In recent years, with the development of convolutional neural network, significant breakthroughs have been made in object detection. This paper describes in detail the classification of object detection algorithms based on deep learning. The algorithms are mainly divided into one-stage object algorithm and two-stage object algorithm, and the general data sets and performance indicators of object detection.
Image segmentation plays an important role in a pre-processing phase of images having as objective a partition of the image into components or regions of interest for a more detailed analysis of one or more of these regions. Image segmentation may also be used as a pre-processing phase for a better image de-noising or de-blurring that will be done in a separate image processing phase. In this article we study mostly theoretical concepts and some experimental results for evaluation of some image segmentation techniques and their role for a better analysis of image details.

| DOI: 10.17148/IJIREEICE.2022.10542