Article Preview
TopIntroduction
Target detection is a very popular research topic in the field of computer vision. It mainly deals with the matching and segmentation of objects of interest in motion video images, and finally gets the target information and motion trajectory (Zaghari et al., 2021). Motion image target detection can provide a lot of useful data support for computer analysis. Therefore, it is widely used in intelligent transportation, video surveillance, military, and other fields.
After the application of a self-convolutional neural network to motion image target detection, the detection idea of researchers was broadened for target detection in video motion images. The positioning information of the detected image target is processed as a linear regression problem, and a detection process of numerical processing is constructed (Sri & Esther, 2020). As a part of computer vision processing, a standardized processing flow has been formed in target detection. Taking the extracted feature point attributes as the basis of constructing the sliding window can improve the performance of the existing image detection methods (Zibang, 2019). However, due to the poor performance of traditional camera equipment and the interference of light, occlusion, and other external factors in the camera process, it is more difficult to detect high-precision targets. Therefore, a more efficient and accurate moving image target detection method is needed.
To address this problem, Kou et al. (2019) proposed a target detection method based on multi-scale uniform features in multispectral images. The local features of infrared point targets are extracted from the uniformity of gray difference distribution, and the target detection results are output through image fusion detection. The detection efficiency of this method is good. Therefore, the detection accuracy needs to be improved. Deng et al. (2018) proposed a motion point target detection method to detect targets from infrared sequence images. An improved spatiotemporal all-variable model is designed for background prediction, and the predicted background is subtracted from the corresponding sequence of images to obtain the segmented image. Finally, the target is detected according to the product of the segmented image and the time comparison filter. The detection accuracy of this method is good. However, the detection efficiency needs to be improved.
Based on the existing related literature, in this paper, the improved YOLO algorithm will be applied to motion image target detection. Using deformable convolution network processing to improve YOLO algorithm, motion image target detection is finally realized through image segmentation and detection frame construction. Experimental results show that the proposed algorithm has better detection performance. The main contributions of this paper are as follows: (1) Using deformable convolution network to improve YOLO algorithm, the improved YOLO algorithm can better adapt to objects with different scales or deformation degrees, and improve the detection effect.(2) The detection frame of the motion image is constructed, the detection range of the detection frame is controlled, and the motion image objects are classified.(3)The image pixels in the detection frame are compressed into cell structure, the gradient and amplitude parameters of the image are calculated, and the target detection results are determined according to the sliding triggered numerical interval.(4) The results of different data sets show that the proposed motion image target detection algorithm based on the improved YOLO algorithm can detect motion image targets accurately and efficiently.