基于改进YOLOv7的杂交大豆苗期胚轴颜色检测模型OACSTPCD

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 F,(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 F,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.