S-YOLOv10-SIC
In order to realize intelligent and accurate campus risk detection, this paper proposes an improved YOLOv10 algorithm that integrates Self-Calibrated Illumination algorithm. The algo-rithm optimizes the loss function by introducing an auxiliary bounding box, and accelerates model convergence. StarNet is employed to enhance the original network structure, feature extraction capability, and decrease parameter count and calculations.
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To solve the problem of accurate recognition and picking of tea by tea picking robot, this study proposes a S-YOLOv10-SIC algorithm that integrates slice-assisted hyper-inference algorithm. This algorithm enhances the YOLOv10 network by introducing Space-to-Depth Convolution, asymptotic feature pyramid network, and Inner-IoU. These improvements reduce the loss of detailed information in long-distance and low-resolution images, improve key layer saliency, optimize non-adjacent layer fusion, enhance model convergence speed, and increase model universality.
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