Abstract
Intelligent surveillance is an important management method for the construction and operation of power stations such as wind power and solar power. The identification and detection of equipment, facilities, personnel, and behaviors of personnel are the key technology for the ubiquitous electricity The Internet of Things. This paper proposes a video solution based on support vector machine and histogram of oriented gradient (HOG) methods for pedestrian safety problems that are common in night driving. First, a series of image preprocessing methods are used to optimize night images and detect lane lines. Second, an image is divided into intelligent regions to be adapted to different road environments. Finally, the HOG and support vector machine methods are used to optimize the pedestrian image on a Linux system, which reduces the number of false alarms in pedestrian detection and the workload of the pedestrian detection algorithm. The test results show that the system can successfully detect pedestrians at night. With image preprocessing optimization, the correct rate of nighttime pedestrian detection can be significantly improved, and the correct rate of detection can reach 92.4%. After the division area is optimized, the number of false alarms decreases significantly, and the average frame rate of the optimized video reaches 28 frames per second.
Highlights
The development of renewable energy to reduce reliance on traditional thermal, coal, or nuclear power generation methods has been the goal of governments in recent years (Johansson, 1975)
To meet the demand for intelligent pedestrian detection at night, this paper proposes a vehicle-mounted nighttime pedestrian detection algorithm based on support vector machine (SVM) and histogram of oriented gradient (HOG)
The nighttime pedestrian detection system proposed in this paper can be applied to automobile driving, which is of great significance in night driving and provides important research value in the future development of unmanned driving system
Summary
The development of renewable energy to reduce reliance on traditional thermal, coal, or nuclear power generation methods has been the goal of governments in recent years (Johansson, 1975). Wind power and solar power facilities are widely distributed in deserted or sparsely populated areas, where external communication conditions are poor, people, materials, and machines are dynamically concentrated, lead to a lot of safety risks at the construction and operation. It affects the normal operation of the power plant and causes serious losses to the enterprise (Aki et al, 2016; Schmarzo, 2015; Zeid and Davis, 2014). This paper takes the typical application demand of nocturnal pedestrian detection as the research object, which is of high economic value and practical significance (Han et al, 2019; Pease, 2015)
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