| 基于改进YOLO v4的群体棉种双面破损检测方法 |
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| 引用本文： | 王巧华,顾伟,蔡沛忠,张洪洲.基于改进YOLO v4的群体棉种双面破损检测方法[J].农业机械学报,2022,53(1):389-397. |
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| 作者姓名： | 王巧华 顾伟 蔡沛忠 张洪洲 |
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| 作者单位： | 华中农业大学工学院,武汉430070;农业农村部长江中下游农业装备重点实验室,武汉430070;华中农业大学工学院,武汉430070 |
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| 基金项目： | 国家自然科学基金项目（31760340）、新疆生产建设兵团南疆重点领域科技支撑计划项目（2018DB001）、华中农业大学-塔里木大学联合基金项目（HNLH202002）和中国农业大学-塔里木大学联合基金项目（TDZNLH201703） |
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| 摘 要： | 针对研究人员难以利用计算机视觉对棉种这类尺寸较小的物体进行双面检测,导致检测效果不佳的问题,设计了一款新型棉种检测分选装置,利用亚克力板在强光和白色背景下透明的特点,将棉种通过上料装置滑入透明亚克力板的凹槽中,随着转盘的转动,同一批棉种的正反两面图像分别由2个不同位置的CCD相机采集得到.利用改进YOLO v4的目标检...
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| 关 键 词： | 脱绒棉种 破损检测 双CCD相机 YOLO v4 图像识别 |
| 收稿时间： | 2021/1/10 0:00:00 |
Detection Method of Double Side Breakage of Population Cotton Seed Based on Improved YOLO v4 |
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| WANG Qiaohu,GU Wei,CAI Peizhong,ZHANG Hongzhou.Detection Method of Double Side Breakage of Population Cotton Seed Based on Improved YOLO v4[J].Transactions of the Chinese Society of Agricultural Machinery,2022,53(1):389-397. |
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| Authors: | WANG Qiaohu GU Wei CAI Peizhong ZHANG Hongzhou |
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| Institution: | Huazhong Agricultural University |
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| Abstract: | Computer vision is one of the commonly used technical methods in the field of cotton seed detection. It has been widely used in the field of non-destructive inspection of agricultural products. However, in most cases, it is difficult for researchers to use computer vision to detect small-sized objects such as cotton seeds on both sides. The detection effect is not good. Aiming at this problem, a type of cotton seed detection and sorting device was designed, which used the transparent characteristics of the acrylic plate under strong light and white background to slide the cotton seed into the groove of the transparent acrylic plate through the feeding device. With the rotation of the turntable, the front and back images of the same batch of cotton were collected by two CCD cameras at different positions. The improved YOLO v4 target detection algorithm was used to detect damaged cotton seeds. The experimental results showed that the model established by this method can detect damaged and intact cotton seeds in the population cotton seeds with an accuracy of 95.33%, recall rate of 96.31%, and missed detection rate of 0. The detection effect was better than that of the original YOLO v4 network, respectively. The proposed method realized the identification of the damage of double-sided group cotton seed, and provided technical support for the subsequent research and development of related delinted cotton seed intelligent detection equipment. |
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| Keywords: | delinted cotton seeds breakage detection double CCD camera YOLO v4 image recognition |
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