Machine vision selection of attention region object detection based on significant graph

This research aims to address the issue of detecting small targets in a complex background, particularly focusing on the low detection efficiency caused by the overwhelming background information in image data and the occurrence of missed and false detections due to the small size of the targets. By combining bottom-up data-driven and top-down knowledge-driven approaches, a novel machine vision selective attention computational model is proposed to enhance the accuracy and efficiency of detection.Research on attention area labeling methods, devices, equipment, and media based on machine vision to solve the problems of low processing efficiency, poor detection accuracy, and susceptibility to redundant data interference in small object detection in large backgrounds in existing machine vision systems. The processing flow is as follows: obtain the original image within the preset range of the detection object, obtain prior knowledge of the detection object, and extract multiple key feature sets from the original image based on the prior knowledge; Generate multiple corresponding feature maps based on key feature sets, and fuse the feature maps corresponding to each key feature set to obtain the target saliency map; Select gaze points from the target saliency map according to the detection criteria of the detection object, obtain multiple sub images based on the gaze points, determine whether the sub images contain the detection object, and mark the sub images containing the detection object as suspicious areas.Experimental results demonstrate that compared to traditional algorithms, this model significantly reduces false alarm rate while ensuring a high recognition rate, thus enhancing the accuracy and efficiency of detection. The success of this study illustrates the potential of improving the task relevance and detection efficiency of machine vision systems by integrating diverse visual attention mechanisms.