Abstract
A small object contamination detection method (SRW-YOLO) integrating SPD-Conv and structural reparameterization was proposed to address the problem of the difficulty in the detection of small object contaminated areas of Lentinula Edodes logs. First, the SPD (space-to-depth)-Conv was used to improve the MP module to enhance the learning of effective features of Lentinula Edodes log images and prevent the loss of small object contamination information. Meanwhile, RepVGG was introduced into the ELAN structure to improve the efficiency and accuracy of inference on the contaminated regions of Lentinula Edodes logs through structural reparameterization. Finally, the boundary regression loss function was replaced with the WIoU (Wise-IoU) loss function, which focuses more on ordinary-quality anchor boxes and makes the model output results more accurate. In this study, the measures of Precision, Recall, and reached 97.63%, 96.43%, and 98.62%, respectively, which are 4.62%, 3.63%, and 2.31% higher compared to those for YOLOv7. Meanwhile, the SRW-YOLO model detects better compared with the current advanced one-stage object detection model.
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Wu, Q., Chen, X., Shang, S., Zhang, F., Tan, W. (2024). A Method for Small Object Contamination Detection of Lentinula Edodes Logs Integrating SPD-Conv and Structural Reparameterization. In: Jin, H., Yu, Z., Yu, C., Zhou, X., Lu, Z., Song, X. (eds) Green, Pervasive, and Cloud Computing. GPC 2023. Lecture Notes in Computer Science, vol 14503. Springer, Singapore. https://doi.org/10.1007/978-981-99-9893-7_3
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