{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T15:17:35Z","timestamp":1778339855597,"version":"3.51.4"},"reference-count":37,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2022,9,26]],"date-time":"2022-09-26T00:00:00Z","timestamp":1664150400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2020YFB1709904"],"award-info":[{"award-number":["2020YFB1709904"]}]},{"name":"National Key Research and Development Program of China","award":["2020YFB1709901"],"award-info":[{"award-number":["2020YFB1709901"]}]},{"name":"National Key Research and Development Program of China","award":["51905460"],"award-info":[{"award-number":["51905460"]}]},{"name":"National Key Research and Development Program of China","award":["51975495"],"award-info":[{"award-number":["51975495"]}]},{"name":"National Key Research and Development Program of China","award":["2021A1515012286"],"award-info":[{"award-number":["2021A1515012286"]}]},{"name":"National Natural Science Foundation of China","award":["2020YFB1709904"],"award-info":[{"award-number":["2020YFB1709904"]}]},{"name":"National Natural Science Foundation of China","award":["2020YFB1709901"],"award-info":[{"award-number":["2020YFB1709901"]}]},{"name":"National Natural Science Foundation of China","award":["51905460"],"award-info":[{"award-number":["51905460"]}]},{"name":"National Natural Science Foundation of China","award":["51975495"],"award-info":[{"award-number":["51975495"]}]},{"name":"National Natural Science Foundation of China","award":["2021A1515012286"],"award-info":[{"award-number":["2021A1515012286"]}]},{"name":"Guangdong Basic and Applied Basic Research Foundation","award":["2020YFB1709904"],"award-info":[{"award-number":["2020YFB1709904"]}]},{"name":"Guangdong Basic and Applied Basic Research Foundation","award":["2020YFB1709901"],"award-info":[{"award-number":["2020YFB1709901"]}]},{"name":"Guangdong Basic and Applied Basic Research Foundation","award":["51905460"],"award-info":[{"award-number":["51905460"]}]},{"name":"Guangdong Basic and Applied Basic Research Foundation","award":["51975495"],"award-info":[{"award-number":["51975495"]}]},{"name":"Guangdong Basic and Applied Basic Research Foundation","award":["2021A1515012286"],"award-info":[{"award-number":["2021A1515012286"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>To handle the problem of low detection accuracy and missed detection caused by dense detection objects, overlapping, and occlusions in the scenario of complex construction machinery swarm operations, this paper proposes a multi-object detection method based on the improved YOLOv4 model. Firstly, the K-means algorithm is used to initialize the anchor boxes to improve the learning efficiency of the depth features of construction machinery objects. Then, the pooling operation is replaced with dilated convolution to solve the problem that the pooling layer reduces the resolution of feature maps and causes a high missed detection rate. Finally, focus loss is introduced to optimize the loss function of YOLOv4 to improve the imbalance of positive and negative samples during the model training process. To verify the effectiveness of the above optimizations, the proposed method is verified on the Pytorch platform with a self-build dataset. The experimental results show that the mean average precision(mAP) of the improved YOLOv4 model for multi-object detection of construction machinery can reach 97.03%, which is 2.16% higher than that of the original YOLOv4 detection network. Meanwhile, the detection speed is 31.11 fps, and it is reduced by only 0.59 fps, still meeting the real-time requirements. The research lays a foundation for environment perception of construction machinery swarm operations and promotes the unmanned and intelligent development of construction machinery swarm operations.<\/jats:p>","DOI":"10.3390\/s22197294","type":"journal-article","created":{"date-parts":[[2022,9,28]],"date-time":"2022-09-28T03:30:37Z","timestamp":1664335837000},"page":"7294","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Multi-Object Detection Method in Construction Machinery Swarm Operations Based on the Improved YOLOv4 Model"],"prefix":"10.3390","volume":"22","author":[{"given":"Liang","family":"Hou","sequence":"first","affiliation":[{"name":"Department of Mechanical and Electrical Engineering, Xiamen University, Xiamen 361102, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chunhua","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Mechanical and Electrical Engineering, Xiamen University, Xiamen 361102, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6818-3136","authenticated-orcid":false,"given":"Shaojie","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Mechanical and Electrical Engineering, Xiamen University, Xiamen 361102, China"},{"name":"Shenzhen Research Institute of Xiamen University, Shenzhen 518057, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongjun","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Mechanical and Electrical Engineering, Xiamen University, Xiamen 361102, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiu","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Mechanical and Electrical Engineering, Xiamen University, Xiamen 361102, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,26]]},"reference":[{"key":"ref_1","first-page":"186","article-title":"An improved BR-YOLOv3 object detection network","volume":"47","author":"Huan","year":"2021","journal-title":"Comput. 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