{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,11,6]],"date-time":"2024-11-06T05:24:39Z","timestamp":1730870679018,"version":"3.28.0"},"reference-count":36,"publisher":"Wiley","issue":"8","license":[{"start":{"date-parts":[[2024,7,15]],"date-time":"2024-07-15T00:00:00Z","timestamp":1721001600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Journal of Field Robotics"],"published-print":{"date-parts":[[2024,12]]},"abstract":"Abstract<\/jats:title>Intelligent electric shovels are being developed for intelligent mining in open\u2010pit mines. Complex environment detection and target recognition based on image recognition technology are prerequisites for achieving intelligent electric shovel operation. However, there is a large amount of sand\u2013dust in open\u2010pit mines, which can lead to low visibility and color shift in the environment during data collection, resulting in low\u2010quality images. The images collected for environmental perception in sand\u2013dust environment can seriously affect the target detection and scene segmentation capabilities of intelligent electric shovels. Therefore, developing an effective image processing algorithm to solve these problems and improve the perception ability of intelligent electric shovels has become crucial. At present, methods based on deep learning have achieved good results in image dehazing, and have a certain correlation in image sand\u2013dust removal. However, deep learning heavily relies on data sets, but existing data sets are concentrated in haze environments, with significant gaps in the data set of sand\u2013dust images, especially in open\u2010pit mining scenes. Another bottleneck is the limited performance associated with traditional methods when removing sand\u2013dust from images, such as image distortion and blurring. To address the aforementioned issues, a method for generating sand\u2013dust image data based on atmospheric physical models and CIELAB color space features is proposed. The impact mechanism of sand\u2013dust on images was analyzed through atmospheric physical models, and the formation of sand\u2013dust images was divided into two parts: blurring and color deviation. We studied the blurring and color deviation effect generation theories based on atmospheric physical models and CIELAB color space, and designed a two\u2010stage sand\u2013dust image generation method. We also constructed an open\u2010pit mine sand\u2013dust data set in a real mining environment. Last but not least, this article takes generative adversarial network (GAN) as the research foundation and focuses on the formation mechanism of sand\u2013dust image effects. The CIELAB color features are fused with the discriminator of GAN as basic priors and additional constraints to improve the discrimination effect. By combining the three feature components of CIELAB color space and comparing the algorithm performance, a feature fusion scheme is determined. The results show that the proposed method can generate clear and realistic images well, which helps to improve the performance of target detection and scene segmentation tasks in heavy sand\u2013dust open\u2010pit mines.<\/jats:p>","DOI":"10.1002\/rob.22387","type":"journal-article","created":{"date-parts":[[2024,7,15]],"date-time":"2024-07-15T12:45:11Z","timestamp":1721047511000},"page":"2832-2847","update-policy":"http:\/\/dx.doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A CIELAB fusion\u2010based generative adversarial network for reliable sand\u2013dust removal in open\u2010pit mines"],"prefix":"10.1002","volume":"41","author":[{"given":"Xudong","family":"Li","sequence":"first","affiliation":[{"name":"School of Mechanical Engineering Dalian University of Technology Dalian China"},{"name":"Key Laboratory for Micro\/Nano Technology and System of Liaoning Province Dalian University of Technology Dalian China"}]},{"given":"Chong","family":"Liu","sequence":"additional","affiliation":[{"name":"Key Laboratory for Micro\/Nano Technology and System of Liaoning Province Dalian University of Technology Dalian China"},{"name":"Key Laboratory for Digital Design and Intelligent Equipment Technology of Liaoning Province Dalian University of Technology Dalian China"},{"name":"Key Laboratory for Precision and Non\u2010traditional Machining Technology of Ministry of Education Dalian University of Technology Dalian China"}]},{"given":"Yangyang","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering Dalian University of Technology Dalian China"}]},{"given":"Wujie","family":"Li","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering Dalian University of Technology Dalian China"}]},{"given":"Jingmin","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory for Micro\/Nano Technology and System of Liaoning Province Dalian University of Technology Dalian China"},{"name":"Key Laboratory for Digital Design and Intelligent Equipment Technology of Liaoning Province Dalian University of Technology Dalian China"},{"name":"Key Laboratory for Precision and Non\u2010traditional Machining Technology of Ministry of Education Dalian University of Technology Dalian China"}]}],"member":"311","published-online":{"date-parts":[[2024,7,15]]},"reference":[{"key":"e_1_2_7_2_1","doi-asserted-by":"publisher","DOI":"10.5815\/ijisa.2016.08.02"},{"key":"e_1_2_7_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2016.2598681"},{"key":"e_1_2_7_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/jqe.1978.1069864"},{"key":"e_1_2_7_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3034151"},{"key":"e_1_2_7_6_1","doi-asserted-by":"crossref","unstructured":"Dong Y. 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