论文标题
部分可观测时空混沌系统的无模型预测
Plastic Contaminant Detection in Aerial Imagery of Cotton Fields with Deep Learning
论文作者
论文摘要
如果在收获之前未清除，则从道路侧面和缠结在棉花植物上纠结的塑料购物袋最终可能会陷入困境。这样的袋子不仅可能在杜松子酒过程中引起问题，而且还可能体现在棉纤维中，以降低其质量和可销售价值。因此，需要在收获棉花之前检测，定位和取下袋子。手动检测和定位在棉田中的这些袋子是劳动力密集，耗时和昂贵的过程。为了解决这些挑战，我们介绍了使用无人驾驶飞机系统（UAS）获得的RGB（红色，绿色和蓝色）图像的四种变体（Yolov5s，Yolov5s，Yolov5M，Yolov5M，Yolov5L和Yolov5X）的应用。我们还显示了塑料袋颜色的固定效应模型测试以及Yolov5变化的平均精度（AP），平均平均精度（MAP@50）和准确性。此外，我们还展示了塑料袋高度对检测准确性的影响。发现袋子的颜色对所有四个变体的准确性具有显着影响（p <0.001），而在95％的置信度下对yolov5m（p = 0.10）和yolov5x（p = 0.35）的AP没有任何显着影响。同样，Yolov5-Variant对白色袋子的AP（P = 0.11）和准确性（P = 0.73）没有任何显着影响，但它对AP（P = 0.03）和棕色袋子的准确性（P = 0.02）产生了重大影响（P = 0.02），包括对MAP@50（P = 0.01）和PENCERATION SPESE（P <0.0001）。此外，塑料袋的高度对总检测准确性具有显着影响（P <0.0001）。本文报道的发现对于加速在收获前从棉田中取出塑料袋，从而减少最终在棉虫时的污染物量可能很有用。
Plastic shopping bags that get carried away from the side of roads and tangled on cotton plants can end up at cotton gins if not removed before the harvest. Such bags may not only cause problem in the ginning process but might also get embodied in cotton fibers reducing its quality and marketable value. Therefore, it is required to detect, locate, and remove the bags before cotton is harvested. Manually detecting and locating these bags in cotton fields is labor intensive, time-consuming and a costly process. To solve these challenges, we present application of four variants of YOLOv5 (YOLOv5s, YOLOv5m, YOLOv5l and YOLOv5x) for detecting plastic shopping bags using Unmanned Aircraft Systems (UAS)-acquired RGB (Red, Green, and Blue) images. We also show fixed effect model tests of color of plastic bags as well as YOLOv5-variant on average precision (AP), mean average precision (mAP@50) and accuracy. In addition, we also demonstrate the effect of height of plastic bags on the detection accuracy. It was found that color of bags had significant effect (p < 0.001) on accuracy across all the four variants while it did not show any significant effect on the AP with YOLOv5m (p = 0.10) and YOLOv5x (p = 0.35) at 95% confidence level. Similarly, YOLOv5-variant did not show any significant effect on the AP (p = 0.11) and accuracy (p = 0.73) of white bags, but it had significant effects on the AP (p = 0.03) and accuracy (p = 0.02) of brown bags including on the mAP@50 (p = 0.01) and inference speed (p < 0.0001). Additionally, height of plastic bags had significant effect (p < 0.0001) on overall detection accuracy. The findings reported in this paper can be useful in speeding up removal of plastic bags from cotton fields before harvest and thereby reducing the amount of contaminants that end up at cotton gins.
