【摘要】 [目的]提高现有食品生产线核桃仁分选的效率和精度。[方法]基于核桃仁分拣的智能化生产线，提出一种改进的EfficientDet模型用于食品生产线核桃仁智能化分选。通过在主干网络引入卷积注意力机制模块，强化模型对食品区域的聚焦能力。通过改进双向特征金字塔网络，增强模型对不同尺度食品的检测能力。通过Dynamic ReLU激活函数对原激活函数进行优化，增强模型对食品的检测性能，并将优化后的模型部署于食品生产线进行试验验证。[结果]试验方法在核桃仁分选任务中实现对正常、碎壳、黑斑和干瘪核桃仁的精准识别与高效分类，单张图像检测时间为18 ms，平均精度均值达到97.92%，误检率降至1.0%，可有效提高食品生产线自动化水平。[结论]该智能化分选方法有效解决了传统分选效率低和精度差的问题，在食品生产线自动化领域具有良好的应用前景与推广价值。更多还原

【Abstract】 [Objective] To improve the efficiency and accuracy of walnut kernel sorting in existing food production lines. [Methods] Based on the intelligent production line for walnut kernel sorting, an improved EfficientDet model is proposed for the intelligent sorting of walnut kernels in food production lines. A convolutional attention mechanism module is introduced into the backbone network to strength the ability of the model to focus on food regions. The bidirectional feature pyramid network is improved to enhance the detection ability of the model for different scales of food. The original activation function is optimized through Dynamic ReLU activation function to enhance the detection performance of the model for food, and the optimized model is deployed in food production for experimental verification. [Results] The experimental method achieves precise recognition and efficient classification of normal, broken shells, black spots, and dried walnut kernels in the walnut kernel sorting task. This method achieves the detection of a single image within 18 ms, with the average accuracy of 97.92% and a false detection rate reduced to 1.0%. This can effectively improve the automation level of the food production line. [Conclusion] This intelligent sorting method effectively solves the problems of low efficiency and poor accuracy of conventional sorting methods, and has good application prospects and promotion value in food production line automation.更多还原