Affiliation:
1. Department of Electrical and Information Engineering, Hebei Jiaotong Vocational and Technical College, Shijiazhuang 050051, China
2. Road Traffic Perception and Intelligent Application Technology R&D Center of Universities in Hebei Province, Shijiazhuang 050051, China
3. School of Technology, Beijing Forestry University, Beijing 100083, China
Abstract
As one of the more difficult problems in the field of computer vision, utilizing object image detection technology in a complex environment includes other key technologies, such as pattern recognition, artificial intelligence, and digital image processing. However, because an environment can be complex, changeable, highly different, and easily confused with the target, the target is easily affected by other factors, such as insufficient light, partial occlusion, background interference, etc., making the detection of multiple targets extremely difficult and the robustness of the algorithm low. How to make full use of the rich spatial information and deep texture information in an image to accurately identify the target type and location is an urgent problem to be solved. The emergence of deep neural networks provides an effective way for image feature extraction and full utilization. By aiming at the above problems, this paper proposes an object detection model based on the mixed attention mechanism optimization of YOLOv5 (MAO-YOLOv5). The proposed method fuses the local features and global features in an image so as to better enrich the expression ability of the feature map and more effectively detect objects with large differences in size within the image. Then, the attention mechanism is added to the feature map to weigh each channel, enhance the key features, remove the redundant features, and improve the recognition ability of the feature network towards the target object and background. The results show that the proposed network model has higher precision and a faster running speed and can perform better in object-detection tasks.
Funder
High-level Talents Funding Project of Hebei Province
Hebei Provincial Higher Education Science and Technology Research Key Project
Subject
Electrical and Electronic Engineering,Computer Networks and Communications,Hardware and Architecture,Signal Processing,Control and Systems Engineering
Cited by
2 articles.
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