Target Detection for Motion Images Using the Improved YOLO Algorithm

Abstract

The images of motion states are time-varying, and when actually detecting their internal motion targets, the formed detection frames overlap, resulting in small confidence values for the detection frames and low accuracy of the detection results. To address this problem, the authors propose a target detection for motion image using the improved YOLO algorithm. First, the YOLO algorithm is improved using deformable convolution; the edge weights of the front and back views within the image are collated, and the motion image is segmented using the improved YOLO algorithm. Second, the structure formed by the initial convolution is used as the initial detection frame structure, the parallel cross-ratio value is set, the overlap generated by the detection frame is controlled, the parameters of the detection frame compression processing are output, the threshold trigger value relationship is constructed, and finally, the detection of the motion image target is realized. The results show that the target false detection rate of the proposed method is only about 15%. The detection a priori frame height value is 80 pixels, and the average detection time consumed is 6.8ms, which proves that the proposed algorithm can be widely used in motion image target detection to improve the detection level.

An Efficient Rep-Style Gaussian–Wasserstein Network: Improved UAV Infrared Small Object Detection for Urban Road Surveillance and Safety

“…With the rapid development of information processing [1] and integrated circuit [2,3] technology, the cost of advanced scientific and technological applications (e.g., unmanned aerial vehicle (UAV) remote sensing [4][5][6][7] and deep learning networks [8][9][10]) is decreasing, leading to many notable social and industrial improvements. These improvements enable scientists to develop a variety of new products.…”