Abstract
Many studies on the object detection emphasizes the accuracy of the algorithms themselves, while the requirement of real-time processing can be addressed by the usage of “you only look once” (YOLO) model. However, the reliably of machine vision is still a problem since some practical issues are not addressed properly, such as variation of light intensity, reflection of light on the surface and interference of shooting background. In this paper, we address above problems by developing a vision system with YOLO algorithm for object detection, segmentation and localization. A segmentation approach is adopted on the model outputs to extract the object to be detected from the background, under the premise of enhancing the adaptability of the YOLO model to environmental changes. Thus, the influence of background and light-sensitive factors on localization is removed even in extreme lighting conditions. An experimental platform is built based on a pair of low-cost cameras, which verifies the effectiveness of proposed method.
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Funding
This work was supported in part by the “Leading Goose” R&D Program of Zhejiang Province under Grant 2022C01114, in part by the National Natural Science Foundation of China under Grant U20A20282 and Grant 51905523, in part by the Key Research and Development Plan of Zhejiang Province under Grant 2021C01069, in part by the Ningbo Science and Technology Innovation Key Projects under Grant 2018B10069, in part by the Strategic Priority Research Program of the Chinese Academy of Sciences under Grant XDA22000000.
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Ge, W., Chen, S., Hu, H. et al. Detection and localization strategy based on YOLO for robot sorting under complex lighting conditions. Int J Intell Robot Appl 7, 589–601 (2023). https://doi.org/10.1007/s41315-023-00285-z
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DOI: https://doi.org/10.1007/s41315-023-00285-z