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- W4361188672 abstract "Abstract Aiming at the problems of small target, variable morphology and dense distribution in the detection of microalgae cells in ship ballast water, this paper proposes a microalgae detection algorithm based on the YOLOv5s and Mixed Residual Swin Transformer(MRST). Firstly, to improve microalgae detection in dense scenes, this paper proposed a novel structure of Swin transformer with residual connectivity combined with feature extraction network of Yolov5s. It can enhance the model's encoding of relative location features to improve global capture performance. Secondly, to refine the extraction of microalgal features, an improved lightweight attention module is proposed to enhance multi-morphic feature recognition. Thirdly, since microalgae are mostly small targets, we fine-tuned the prediction mechanism, which can improve the small target localization ability and reduce the network parameters at the same time. Finally, to further improve the detection in dense microalgae scenarios, a novel prediction box processing paradigm CP-cluster is introduced to optimize the post-processing of the network. The experimental results indicate that under identical conditions, the mAP-IOU@0.5 and mAP-IOU@[0.5:0.95] of the proposed MRST-YOLO algorithm are 99.6% and 83.5%, respectively. The algorithm has the capability to achieve real-time detection, which greatly enhances its ability to identify microalgae species with better accuracy and efficiency." @default.
- W4361188672 created "2023-03-31" @default.
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- W4361188672 date "2023-03-28" @default.
- W4361188672 modified "2023-09-23" @default.
- W4361188672 title "MRST-YOLO: A Novel Microalgae Detection Method Based On YOLOv5s and Mixed Residual Swin Transformer" @default.
- W4361188672 doi "https://doi.org/10.21203/rs.3.rs-2729685/v1" @default.
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