基于改进YOLOv7的杂交大豆苗期胚轴颜色检测模型

为构建田间杂交大豆胚轴颜色检测模型，以大田场景下的大豆植株为研究对象，利用自走式大豆表型信息采集平台获取图像数据并构建杂交大豆胚轴颜色数据集，使用不同目标检测模型（SSD、Faster R-CNN、YOLOv3、YOLOv4、YOLOv5、YOLOX和YOLOv7）对杂交大豆胚轴颜色数据集进行检测，将模型分数（F1）、平均精度均值（mAP）及检测速度3个指标用于评估不同模型在杂交大豆胚轴颜色检测中的性能。在YOLOv7网络中添加CARAFE特征上采样算子、SE注意力机制模块和WIoU位置损失函数，建立杂交大豆胚轴颜色检测模型YOLOv7-CSW，并利用改进模型对杂交大豆胚轴颜色数据集进行消融试验。结果表明：1)YOLOv7模型的F1(0.92)与mAP(94.3%)均显著高于其他模型；2)YOLOv7模型的检测速度为58帧/s，低于YOLOv5和YOLOX，检测速度可以满足田间实时检测任务需求；3)YOLOv7-CSW模型比YOLOv7模型的F1和mAP分别升高0.04和2.6%;4)YOLOv7-CSW模型比YOLOv7模型检测速度升高了5帧/s，可以实现杂交大豆胚轴颜色实时检测。综...

Color detection model of hybrid soybean hypocotyl based on an improved YOLOv7 object detection model

In order to build a color detection model of hybrid soybean hypocotyl， soybean plants in natural growing environment were used as research objects. This study used a self-propelled soybean phenotyping information collection platform to obtain soybean plant image data in the farmland， and built hybrid soybean hypocotyl color dataset. Color dataset of hybrid soybean hypocotyl were detected with different object detection models （SSD， Faster R-CNN， YOLOv3， YOLOv4， YOLOv5， YOLOX and YOLOv7）. The model score （F₁）， mean of average precision （mAP）， and detection speed were compared to evaluate the performance of different models in color detection of hybrid soybean hypocotyl. A color detection model of hybrid soybean hypocotyl， YOLOv7-CSW， was established by adding CARAFE feature up sampling operator， SE attention mechanism module and WIoU box loss function into the YOLOv7 network. The improved model was used to perform ablation tests on the hybrid soybean hypocotyl color dataset. The results showed that： 1） The F₁ （0.92） and mAP （94.3%） of YOLOv7 model were significantly higher than those of other models； 2） The detection speed of YOLOv7 model was 58 fps， which was only lower than YOLOv5 and YOLOX， but the detection speed could meet the requirements for real-time detection tasks； 3） Compared with YOLOv7 model， the F₁ and mAP of YOLOv7-CSW model were increased by 0.04 and 2.6% respectively； 4） The detection speed of YOLOv7-CSW model was 5 fps higher than YOLOv7 model， and it realized real-time detection of hybrid soybean hypocotyl color. To sum up， YOLOv7-CSW model could better obtain hypocotyl color features and accurately detect the object position， improved the object detection performance in complex farmland environments， and realized rapid and accurate detection of hypocotyl color of field hybrid soybean.