基于改进YOLOv7模型的复杂环境下鸭蛋识别定位

Improved YOLOv7 model for duck egg recognition and localization in complex environments

在干扰、遮挡等复杂环境下,对鸭蛋进行快速、准确识别定位是开发鸭蛋拾取机器人的关键技术,该研究提出一种基于改进YOLOv7(you only look once)模型的复杂环境鸭蛋检测方法,在主干网络加入卷积注意力模块(CBAM,convolutional block attention module),加强网络信息传递,提高模型对特征的敏感程度,减少复杂环境对鸭蛋识别干扰;利用深度可分离卷积(DSC,depthwise separable convolution)、调整空间金字塔池化结构(SPP,spatial pyramid pooling),降低模型参数数量和运算成本。试验结果表明,与SSD、YOLOv4、YOLOv5_M以及YOLOv7相比,改进YOLOv7模型的F1分数(F1 score)分别提高了8.3、10.1、8.7和7.6个百分点,F1分数达95.5%,占内存空间68.7 M,单张图片检测平均用时0.022 s。与不同模型在复杂环境的检测对比试验表明,改进的YOLOv7模型,在遮挡、簇拥、昏暗等复杂环境下,均能对鸭蛋进行准确快速的识别定位,具有较强鲁棒性和适用性。该研究可为后续开发鸭蛋拾取机器人提供技术支撑。

Robot technology has been gradually applied in modern agriculture in recent years.Among them,a duck egg is one of the most important agricultural products during food processing.However,the current collection of duck eggs can usually require a large amount of manual labor,leading to the time-consuming and labor-intensive task.A smart robot has been developed to automatically collect the duck eggs,in order to improve the production efficiency for the harvesting cost-saving.Specifically,an important technical challenge of harvesting robots can be the rapid and accurate identification and positioning of duck eggs,especially under complex environments,such as occlusion,crowding,and darkness.In this study,duck egg detection was proposed for complex environments using an improved YOLOv7 model.A convolutional attention module(CBAM) was added to the backbone network.The network information transmission was enhanced for better sensitivity to the specific features,while the interference of complex environments was reduced on the duck egg recognition.At the same time,the depth-wise separable convolution(DSC) was utilized to adjust the spatial pyramid pooling(SPP),in order to reduce the number of model parameters and operation costs.A high accuracy was achieved to identify and locate the duck eggs under complex environments,thus providing technical support for the development of harvesting robots.Some materials(such as feathers,straw,and sediment) were also used to simulate the complex environment of the duck house.A duck egg image collection platform was then constructed to evaluate the accuracy and environmental adaptability of the model.Actual duck eggs were photographed in the duck house of Wuhan Yujia Bay Duck Farm using an Honor HLK-AL10 camera for both simulated and actual conditions.The duck egg image data was collected,including the multiple angles,positions,different distances and occlusion forms.A total of 2 600 JPG format images were divided into the training set(1 560 images),validation set(520 images),and test set(520 images),according to a 6:2:2 ratio.At the same time,data augmentation was performed on the training set,in order to improve the robustness and generalization ability of the model.The following procedures were used:1) To add 12% Gaussian noise.2) To add 2.5% salt and pepper noise.3) To set the image gains a=0.3 and a=0.5 to change image brightness.After that,the improved YOLOv7 model was trained with an iteration number of 150.Test results show that the improved YOLOv7 model increased the F1 score by 8.3,10.1,8.7,and 7.6 percentage points,respectively,and the F1 score reached 95.5%,compared with the common detection models,such as SSD,YOLOv4,YOLOv5_M,and YOLOv7.The occupied memory space was only 68.7 M,while the average time was 0.022 s for the single-image detection,and the average precision value(mAP,Mean Average Precision) was 85.2%.There were no missed or false detections for the feather occlusion or clustering of duck eggs,with an average confidence level of 93.6% and 85.7%,respectively.The more accurate detection was achieved in the improved YOLOv7 model under the complex environments,indicating superior performance.