{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T01:09:25Z","timestamp":1784509765172,"version":"3.55.0"},"reference-count":67,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2024,3,14]],"date-time":"2024-03-14T00:00:00Z","timestamp":1710374400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R&amp;D Program for the 14th Five-Year Plan","award":["SQ2022YFC3000083"],"award-info":[{"award-number":["SQ2022YFC3000083"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>With the continuous development of deep learning, the application of object detection based on deep neural networks in the coal mine has been expanding. Simultaneously, as the production applications demand higher recognition accuracy, most research chooses to enlarge the depth and parameters of the network to improve accuracy. However, due to the limited computing resources in the coal mining face, it is challenging to meet the computation demands of a large number of hardware resources. Therefore, this paper proposes a lightweight object detection algorithm designed specifically for the coal mining face, referred to as CM-YOLOv8. The algorithm introduces adaptive predefined anchor boxes tailored to the coal mining face dataset to enhance the detection performance of various targets. Simultaneously, a pruning method based on the L1 norm is designed, significantly compressing the model\u2019s computation and parameter volume without compromising accuracy. The proposed algorithm is validated on the coal mining dataset DsLMF+, achieving a compression rate of 40% on the model volume with less than a 1% drop in accuracy. Comparative analysis with other existing algorithms demonstrates its efficiency and practicality in coal mining scenarios. The experiments confirm that CM-YOLOv8 significantly reduces the model\u2019s computational requirements and volume while maintaining high accuracy.<\/jats:p>","DOI":"10.3390\/s24061866","type":"journal-article","created":{"date-parts":[[2024,3,15]],"date-time":"2024-03-15T04:47:05Z","timestamp":1710478025000},"page":"1866","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["CM-YOLOv8: Lightweight YOLO for Coal Mine Fully Mechanized Mining Face"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-9276-9549","authenticated-orcid":false,"given":"Yingbo","family":"Fan","sequence":"first","affiliation":[{"name":"Institute of Remote Sensing and Geographic Information Systems, Peking University, No. 5 Summer Palace Road, Beijing 100871, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shanjun","family":"Mao","sequence":"additional","affiliation":[{"name":"Institute of Remote Sensing and Geographic Information Systems, Peking University, No. 5 Summer Palace Road, Beijing 100871, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4763-1817","authenticated-orcid":false,"given":"Mei","family":"Li","sequence":"additional","affiliation":[{"name":"Institute of Remote Sensing and Geographic Information Systems, Peking University, No. 5 Summer Palace Road, Beijing 100871, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6146-4392","authenticated-orcid":false,"given":"Zheng","family":"Wu","sequence":"additional","affiliation":[{"name":"Institute of Remote Sensing and Geographic Information Systems, Peking University, No. 5 Summer Palace Road, Beijing 100871, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jitong","family":"Kang","sequence":"additional","affiliation":[{"name":"Institute of Remote Sensing and Geographic Information Systems, Peking University, No. 5 Summer Palace Road, Beijing 100871, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,3,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1004","DOI":"10.1016\/j.jclepro.2018.04.051","article-title":"Life-cycle assessment for coal-based methanol production in China","volume":"188","author":"Li","year":"2018","journal-title":"J. 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