基于改进CNN的苹果缺陷检测方法研究

目的:解决现有苹果缺陷检测方法存在的精度低、效率差等问题。方法:基于水果图像采集系统，提出一种改进的卷积神经网络用于苹果表面缺陷检测；引入深度可分离卷积代换原网络标准卷积，提高特征提取速度；引入Leaky ReLU激活函数代替ReLU激活函数，提高计算效率和精度；引入全局平均池化替换全连接层，降低网络模型的计算量；并在每层卷积后加入批量归一化层，通过试验与常规方法进行对比分析，验证其优越性。结果:与常规方法相比，所提方法在苹果缺陷检测中具有较高的检测准确率和速度，且模型参数量少，准确率达99.60%,检测速度(每秒帧数)达526,模型参数量为389 072。结论:该苹果缺陷检测方法能有效降低模型参数和检测时间，具有较高的准确率和速度。

Research on apple defect detection method based on improved CNN

Objective: To solve the problems of low accuracy and poor efficiency in existing apple defect detection methods. Methods: Based on a fruit image acquisition system, an improved convolutional neural network was proposed for detecting surface defects in apples. Deep separable convolution was Introduced to replace the original network standard convolution, to improve the speed of feature extraction. The Leaky ReLU activation function was introduced to replace the ReLU activation function to improve the calculation efficiency and accuracy. Global average pooling was introduced to replace the fully connected layer, to reduce the computational complexity of the network model. After each layer of convolution, a batch normalization layer was added, and its superiority was verified through comparative analysis between experiments and conventional methods. Results: Compared with conventional methods, the proposed method had higher detection accuracy and speed in apple defect detection, and had fewer model parameters, with an accuracy rate of 99.60%, a detection speed of 526 FPS, and a model parameter quantity of 389 072. Conclusion: This apple defect detection method can effectively reduce model parameters and detection time, with high accuracy and speed.