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With the ever-increasing advance of artificial neural networks (ANN), researchers have proposed numerous ANN-based visual object detection methods that have proven to be effective. However, networks with cumbersome structures do not befit the real-time scenarios in M&amp;RS, necessitating the techniques of model compression. In the paper, a novel approach to training light-weight visual object detection networks is developed by revisiting knowledge distillation. Traditional knowledge distillation methods are oriented towards image classification is not compatible with object detection. Therefore, a variant of knowledge distillation is developed and adapted to a state-of-the-art keypoint-based visual detection method. Two strategies named as positive sample retaining and early distribution softening are employed to yield a natural adaption. The mutual consistency between teacher model and student model is further promoted through a hint-based distillation. By extensive controlled experiments, the proposed method is testified to be effective in enhancing the light-weight network\u2019s performance by a large margin.<\/jats:p>","DOI":"10.1177\/01423312211022877","type":"journal-article","created":{"date-parts":[[2021,8,13]],"date-time":"2021-08-13T08:53:46Z","timestamp":1628844826000},"page":"2888-2898","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":4,"title":["Revisiting knowledge distillation for light-weight visual object detection"],"prefix":"10.1177","volume":"43","author":[{"given":"Tianze","family":"Gao","sequence":"first","affiliation":[{"name":"Harbin Intitute of Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3308-3255","authenticated-orcid":false,"given":"Yunfeng","family":"Gao","sequence":"additional","affiliation":[{"name":"Harbin Intitute of Technology, China"},{"name":"HIT-Wuhu Robot 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