YOLOv8-SIEMF: A Sub-model Integrated Evaluation and Multi-objective Filtering Approach for Visual Sensing in Telecom Networks

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Abstract

To address the challenges of missed detection and misdetection in tea bud recognition tasks under complex environments, this paper proposes YOLOv8-SIEMF, a novel detection model integrating Sub-models Integral Evaluation (SIE) and Multi-objective Filtering (MF). First, we design a hierarchical detection framework where different sub-models process diverse resolution levels of input images to extract complementary features. An evaluation mechanism is developed to comprehensively fuse the outputs of sub-models by considering detection confidence, box overlap, and image sharpness. Meanwhile, a multi-objective filtering module is introduced to enhance the model’s sensitivity to multi-target clusters and improve edge sharpness in grayscale space, which effectively reduces redundant or invalid detection. Experimental results on a self-built dataset demonstrate that the proposed model outperforms mainstream YOLOv8 variants in terms of precision and recall, achieving superior performance in recognizing fine-grained tea buds under real-field conditions.
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Abstract

To address the challenges of missed detection and misdetection in tea bud recognition tasks under complex environments, this paper proposes YOLOv8-SIEMF, a novel detection model integrating Sub-models Integral Evaluation (SIE) and Multi-objective Filtering (MF). First, we design a hierarchical detection framework where different sub-models process diverse resolution levels of input images to extract complementary features. An evaluation mechanism is developed to comprehensively fuse the outputs of sub-models by considering detection confidence, box overlap, and image sharpness. Meanwhile, a multi-objective filtering module is introduced to enhance the model’s sensitivity to multi-target clusters and improve edge sharpness in grayscale space, which effectively reduces redundant or invalid detection. Experimental results on a self-built dataset demonstrate that the proposed model outperforms mainstream YOLOv8 variants in terms of precision and recall, achieving superior performance in recognizing fine-grained tea buds under real-field conditions. Supplementary Material File (yolov8-siemf a sub-model integrated evaluation and multi-objective filtering approach for visual sensing in telecom networks.docx) - Download - 1.05 MB Information & Authors Information Version history Copyright This work is licensed under a Non Exclusive No Reuse License.

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Authors Metrics & Citations Metrics Article Usage 242views 99downloads Citations Download citation Donghao Cao, Fuchen Huang, Zhengxuan Wei, et al. YOLOv8-SIEMF: A Sub-model Integrated Evaluation and Multi-objective Filtering Approach for Visual Sensing in Telecom Networks. Authorea. 25 April 2025. DOI: https://doi.org/10.22541/au.174556401.17113434/v1 DOI: https://doi.org/10.22541/au.174556401.17113434/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu.

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last seen: 2026-05-20T01:45:00.602351+00:00