基于改进卷积神经网络的在体青皮核桃检测方法

采摘机器人对核桃采摘时，需准确检测到在体核桃目标。为实现自然环境下青皮核桃的精准识别，研究了基于改进卷积神经网络的青皮核桃检测方法。以预训练的VGG16网络结构作为模型的特征提取器，在Faster R-CNN的卷积层加入批归一化处理、利用双线性插值法改进RPN结构和构建混合损失函数等方式改进模型的适应性，分别采用SGD和Adam优化算法训练模型，并与未改进的Faster R-CNN对比。以精度、召回率和F1值作为模型的准确性指标，单幅图像平均检测时间作为速度性能评价指标。结果表明，利用Adam优化器训练得到的模型更稳定，精度高达97.71%，召回率为94.58%，F1值为96.12%，单幅图像检测耗时为0.227s。与未改进的Faster R-CNN模型相比，精度提高了5.04个百分点，召回率提高了4.65个百分点，F1值提升了4.84个百分点，单幅图像检测耗时降低了0.148s。在园林环境下，所提方法的成功率可达91.25%，并且能保持一定的实时性。该方法在核桃识别检测中能够保持较高的精度、较快的速度和较强的鲁棒性，能够为机器人快速长时间在复杂环境下识别并采摘核桃提供技术支撑。

Green Walnut Detection Method Based on Improved Convolutional Neural Network

In order to realize precise detection of green walnut in natural environment, Faster R-CNN algorithm was improved with three methods for higher adaptability, including batch normalization processing of convolution layer, improved RPN using bi-linear interpolation and the establishment of mixed loss function to strengthen the cohesion of the model. The pre-trained VGG16 network was used as feature extractor, and SGD and Adam optimization methods were adopted to training model respectively. The improved Faster R-CNN model was compared with Faster R-CNN model under the same test conditions. Images of different resolution were used as inputs to explore the impact of image sizes on model performance. Precision, recall rate and F1 value were used as the accuracy indexes of the model, and average detection time per image was used to evaluate the speed performance. The investigation showed that the model trained by Adam optimizer was more stable, its precision was 97.71%, the recall rate was 94.58%, and the F1 value was 96.12%. The single image detection time was 0.227s. The precision of improved Faster R-CNN was 5.04 percentage points higher than that of the unimproved Faster R-CNN model, the recall rate was increased by 4.65 percentage points and the F1 index was increased by 4.84 percentage points. Besides, image detection time per image was decreased by 0.148s. The proposed method was verified to obtain the success rate of 91.25% in the walnut garden environment. The proposed method had high precision, fast speed and good robustness for walnut recognition under natural condition, which can provide a basis for the robot to recognize and pick walnuts in a complex environment quickly for a long time.