Feature Recalibration Based Olfactory-Visual Multimodal Model for Fine-Grained Rice Deterioration Detection

Multimodal methods are widely used in rice deterioration detection, which exhibit limited capability in representing and extracting fine-grained abnormal features. Moreover, these methods rely on devices, such as hyperspectral cameras and mass spectrometers, increasing detection costs and prolonging data acquisition time. To address these issues, we propose a feature recalibration based olfactory-visual multimodal model for fine-grained rice deterioration detection. The fine-grained deterioration embedding constructor (FDEC) is proposed to reconstruct the labeled multimodal embedded-feature dataset, enhancing sample representation. The fine-grained deterioration recalibration attention network (FDRA-Net) is proposed to emphasize signal variations and increase sensitivity to fine-grained deterioration on the rice surface. Experiments show that the proposed method achieves a classification accuracy of 99.89%. Compared with state-of-the-art methods, the detection accuracy is improved and the procedure is simplified. Furthermore, field detection demonstrates the advantages of accuracy and operational simplicity. The proposed method can also be extended to other agrifood in agriculture and food industry.

DIVESPOT: Depth Integrated Volume Estimation of Pile of Things Based on Point Cloud

Non-contact volume estimation of pile-type objects has considerable potential in industrial scenarios, including grain, coal, mining, and stone materials. However, using existing method for these scenarios is challenged by unstable measurement poses, significant light interference, the difficulty of training data collection, and the computational burden brought by large piles. To address the above issues, we propose the Depth Integrated Volume EStimation of Pile Of Things (DIVESPOT) based on point cloud technology in this study. For the challenges of unstable measurement poses, the point cloud pose correction and filtering algorithm is designed based on the Random Sample Consensus (RANSAC) and the Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN). To cope with light interference and to avoid the relying on training data, the height-distribution-based ground feature extraction algorithm is proposed to achieve RGB-independent. To reduce the computational burden, the storage space optimizing strategy is developed, such that accurate estimation can be acquired by using compressed voxels. Experimental results demonstrate that the DIVESPOT method enables non-data-driven, RGB-independent segmentation of pile point clouds, maintaining a volume calculation relative error within 2%. Even with 90% compression of the voxel mesh, the average error of the results can be under 3%.