Saliency from High-Level Semantic Image Features

Abstract

Top-down semantic information is known to play an important role in assigning saliency. Recently, large strides have been made in improving state-of-the-art semantic image understanding in the fields of object detection and semantic segmentation. Therefore, since these methods have now reached a high-level of maturity, evaluation of the impact of high-level image understanding on saliency estimation is now feasible. We propose several saliency features which are computed from object detection and semantic segmentation results. We combine these features with a standard baseline method for saliency detection to evaluate their importance. Experiments demonstrate that the proposed features derived from object detection and semantic segmentation improve saliency estimation significantly. Moreover, they show that our method obtains state-of-theart results on (FT, ImgSal, and SOD datasets) and obtains competitive results on four other datasets (ECSSD, PASCAL-S, MSRA-B, and HKU-IS).

Foreign object debris detection based on improved YOLOv5 algorithm

“…The two steps of target determination and target recognition are combined into one, and the position of the prediction box and the category of the prediction box are directly regressed at the output layer. With the continuous improvement and iteration of the target detection algorithm 7 , the YOLO series has developed to YOLOv7.This paper mainly studies YOLOv5. The improvement of YOLOv5 is based on YOLOv4 algorithm.…”