Biomimetic Vision for Zoom Object Detection Based on Improved Vertical Grid Number YOLO Algorithm - Shen, Xinyi; Shi, Guolong; Ren, Huan; Zhang, Wu (Frontiers in Bioengineering and Biotechnology, 2022)
With the development of bionic computer vision for images processing, researchers have easily obtained high-resolution zoom sensing images. The development of drones equipped with high-definition cameras has greatly increased the sample size and image segmentation and target detection are important links during the process of image information. As biomimetic remote sensing images are usually prone to blur distortion and distortion in the imaging, transmission and processing stages, this paper improves the vertical grid number of the YOLO algorithm. Firstly, the light and shade of a high-resolution zoom sensing image are abstracted, and the grey-level cooccurrence matrix extracts feature parameters to quantitatively describe the texture characteristics of the zoom sensing image. The Simple Linear Iterative Clustering (SLIC) superpixel segmentation method is used to achieve the segmentation of light/dark scenes, and the saliency area is obtained. Secondly, a high-resolution zoom sensing image model for segmenting light and dark scenes is established to make the dataset meet the recognition standard. Due to the refraction of the light passing through the lens and other factors, the difference of the contour boundary light and dark value between the target pixel and the background pixel will make it difficult to detect the target, and the pixels of the main part of the separated image will be sharper for edge detection. Thirdly, a YOLO algorithm with an improved vertical grid number is proposed to detect the target in real time on the processed superpixel image array. The adjusted aspect ratio of the target in the remote sensing image modifies the number of vertical grids in the YOLO network structure by using 20 convolutional layers and 5 maximum aggregation layers, which is more accurately adapted to "short and coarse" of the identified object in the information density. Finally, through comparison with the improved algorithm and other mainstream algorithms in different environments, the test results on the aid dataset show that in the target detection of high spatial resolution zoom sensing images, the algorithm in this paper shows higher accuracy than the YOLO algorithm and has real-time performance and detection accuracy.