Yu Haoze, Li Zhuangzi, Li Wei, Guo Wenbo, Li Dong, Wang Lijun, Wu Min, Wang Yong
Beijing Advanced Innovation Center for Food Nutrition and Human Health, College of Engineering, China Agricultural University, 17 Qinghua Donglu, P.O. Box 50, Beijing 100083, China.
School of Electronic and Computer Engineering, Peking University, Shenzhen 518055, China.
Foods. 2023 Jul 29;12(15):2885. doi: 10.3390/foods12152885.
Real-time and accurate awareness of the grain situation proves beneficial for making targeted and dynamic adjustments to cleaning parameters and strategies, leading to efficient and effective removal of impurities with minimal losses. In this study, harvested maize was employed as the raw material, and a specialized object detection network focused on impurity-containing maize images was developed to determine the types and distribution of impurities during the cleaning operations. On the basis of the classic contribution Faster Region Convolutional Neural Network, EfficientNetB7 was introduced as the backbone of the feature learning network and a cross-stage feature integration mechanism was embedded to obtain the global features that contained multi-scale mappings. The spatial information and semantic descriptions of feature matrices from different hierarchies could be fused through continuous convolution and upsampling operations. At the same time, taking into account the geometric properties of the objects to be detected and combining the images' resolution, the adaptive region proposal network (ARPN) was designed and utilized to generate candidate boxes with appropriate sizes for the detectors, which was beneficial to the capture and localization of tiny objects. The effectiveness of the proposed tiny object detection model and each improved component were validated through ablation experiments on the constructed RGB impurity-containing image datasets.
实时准确地了解谷物状况有助于针对性地动态调整清理参数和策略，从而高效且有效地去除杂质，同时损失最小。在本研究中，以收获的玉米为原料，开发了一个专注于含杂质玉米图像的专业目标检测网络，以确定清理作业过程中杂质的类型和分布。在经典的Faster Region Convolutional Neural Network的基础上，引入EfficientNetB7作为特征学习网络的骨干，并嵌入跨阶段特征整合机制，以获得包含多尺度映射的全局特征。通过连续的卷积和上采样操作，可以融合来自不同层次的特征矩阵的空间信息和语义描述。同时，考虑到待检测物体的几何特性并结合图像分辨率，设计并利用自适应区域提议网络（ARPN）为检测器生成具有合适大小的候选框，这有利于微小物体的捕获和定位。通过在构建的RGB含杂质图像数据集上进行消融实验，验证了所提出的微小物体检测模型及每个改进组件的有效性。
