Real-time Target Detection System in Scenic Landscape Based on Improved YOLOv4 Algorithm

With the rapid development of computer vision technology, the use of real-time target detection systems in scenic landscape management and services is increasingly widespread. To enhance the precision and efficiency of real-time target detection in scenic landscapes, this research integrates the fourth version of the You Only Look Once (YOLO) algorithm to construct an optimized real-time target detection system is introduced for scenic landscapes. First, adaptive spatial feature fusion to enhance the fourth version of the You Only Look Once algorithm. Then, the optimized algorithm was combined with OpenCV library, Python OS library, and other hardware and software to design a real-time image recognition system for scenic landscapes. The study results indicated that the proposed optimized algorithm had better recognition performance, and its precision value, recall rate, and F1 value were as high as 0.96, 0.97, and 0.98, respectively. The recognition system, which was developed using an optimization algorithm, demonstrated excellent practical application effect. It displayed stable system operation under four natural landscapes: sunrise, sea of clouds, maple forest, and stone monument, with a stability performance of 0.92, 0.93, 0.92, and 0.94, respectively. Moreover, the system operated remarkably fast, with low operational times of 2.3 s, 0.8 s, 2.9 s, and 1.2 s under these landscapes. In conclusion, the research institute's target detection algorithm has demonstrated excellent performance. Utilizing this algorithm in the detection system can offer technical aid for managing and intelligently detecting scenic landscape images.