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- W4297029083 abstract "At present, large high-precision target detection models are limited by the computing power and memory size of robot embedded devices, and there are problems with parameter redundancy and slow inference speed. This paper proposes an object detection algorithm applied to a basketball robot based on improved yolov5 to address these problems. The improved YOLOv5 algorithm uses GhostNet for lightweight improvement and introduces the Coordinate Attention (CA) mechanism to enhance the expression of learning features in mobile networks to achieve efficient object detection on basketball robots. According to the experimental results, the improved algorithm on YOLOv5s model framework reduces the parameters by 46.84% and the giga floating-point operations per second (GFLOPs) by 48.13%, which has a significant accuracy advantage compared with the algorithm based on other lightweight networks with lightweight improvements. The improved algorithm has high application value in deploying object detection on mobile robot embedded devices." @default.
- W4297029083 created "2022-09-25" @default.
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- W4297029083 date "2022-01-01" @default.
- W4297029083 modified "2023-09-27" @default.
- W4297029083 title "Object Detection Algorithm Based on Improved YOLOv5 for Basketball Robot" @default.
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- W4297029083 doi "https://doi.org/10.1007/978-981-19-6226-4_7" @default.
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