M. Pelletier, J. Wanjura, Jon R. Wakefield, Gregory A. Holt, Neha Kothari
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This research aims to transition the system to a more user-friendly, plug-and-play model by implementing an auto-calibration function. The proposed function dynamically tracks cotton colors while excluding plastic images that could hinder performance. A critical component of this auto-calibration algorithm is the hand intrusion detector, or “HID”, which is discussed in this paper. In the normal operation of a cotton gin, the gin personnel periodically have to clear the machine, which entails running a stick or their arm/hand under the detection cameras. This results in the system capturing a false positive, which interferes with the ability of auto-calibration algorithms to function correctly. Hence, there is a critical need for an HID to remove these false positives from the record. 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引用次数: 0
摘要
皮棉中的塑料污染给美国棉花产业带来了巨大挑战,其中来自约翰迪尔圆形模块收割机的塑料包装是主要污染物。尽管在拆卸模块的过程中人工清除了这些塑料,但仍有一些不可避免地进入了轧棉机的加工系统。为了解决这个问题,我们开发了一种机器视觉检测和清除系统。该系统使用廉价的彩色摄像头来识别轧棉机架喂棉围裙上的塑料,并触发一个装置将塑料从棉花流中排出。然而,该系统由 30-50 台基于 Linux 的 ARM 计算机组成,需要花费大量精力进行校准和调整,对典型的轧棉工人来说存在技术障碍。本研究旨在通过实施自动校准功能,将该系统过渡为更方便用户使用的即插即用模式。建议的功能可动态跟踪棉花颜色,同时排除可能影响性能的塑料图像。这种自动校准算法的一个关键组件是手部入侵探测器,即本文讨论的 "HID"。在轧棉机的正常运行过程中,轧棉人员需要定期清理机器,这就需要用棍子或手臂/手从检测摄像头下穿过。这会导致系统捕捉到假阳性,从而影响自动校准算法的正常运行。因此,亟需一种 HID 来消除记录中的这些误报。自动校准功能的预期效益包括减少设置和维护费用,减少对技术人员的依赖,以及提高轧棉行业对塑料清除系统的采用率。
Cotton Gin Stand Machine-Vision Inspection and Removal System for Plastic Contamination: Hand Intrusion Sensor Design
Plastic contamination in cotton lint poses significant challenges to the U.S. cotton industry, with plastic wrap from John Deere round module harvesters being a primary contaminant. Despite efforts to manually remove this plastic during module unwrapping, some inevitably enters the cotton gin’s processing system. To address this, a machine-vision detection and removal system has been developed. This system uses inexpensive color cameras to identify plastic on the gin stand feeder apron, triggering a mechanism that expels the plastic from the cotton stream. However, the system, composed of 30–50 Linux-based ARM computers, requires substantial effort for calibration and tuning and presents a technological barrier for typical cotton gin workers. This research aims to transition the system to a more user-friendly, plug-and-play model by implementing an auto-calibration function. The proposed function dynamically tracks cotton colors while excluding plastic images that could hinder performance. A critical component of this auto-calibration algorithm is the hand intrusion detector, or “HID”, which is discussed in this paper. In the normal operation of a cotton gin, the gin personnel periodically have to clear the machine, which entails running a stick or their arm/hand under the detection cameras. This results in the system capturing a false positive, which interferes with the ability of auto-calibration algorithms to function correctly. Hence, there is a critical need for an HID to remove these false positives from the record. The anticipated benefits of the auto-calibration function include reduced setup and maintenance overhead, less reliance on skilled personnel, and enhanced adoption of the plastic removal system within the cotton ginning industry.