Superior Attribute Weighted Set for Object Skeleton Detection Using ResNet50 with Edge Based Segmentation Model

Object detection is a method used in computer vision for identifying specific items inside an image or video. Most effective object detection systems make use of machine learning or deep learning. Object detection is a method of computer vision that allows us to find specific things in pictures and videos. Labeling and counting items in a scene, as well as pinpointing their locations and following their movement, are all possible because to object detection's ability to precisely identify and localize them. For instance, it is easy to recognize circles as a distinct class because of their shared characteristic of being spherical. These unique characteristics are used for object class recognition. Facial traits like as skin tone and eye distance are employed in a manner analogous to that used for fingerprinting in order to positively identify a person by their face. The object detection task is typically made much more challenging due to the test images being sampled from a distinct data distribution. Many unsupervised domain adaptation approaches have been presented to solve the difficulties introduced by the discrepancy between the domains of the training and test data. Cross-domain object detection has many applications, including autonomous driving because to the ease with which labels can be generated for a large number of scenes in video games. Object detection methods can be categorized as either neural network-based or non-neural. This research presents a Superior Attribute Weighted Set for Object Skeleton Detection using ResNet50 (SAWS-OSD-ResNet50). The proposed model when compared with the traditional methods performs better in object detection.