Online Detection Method for Rice Processing Quality Based on SDS-YOLOv8: Application to the Hulling Stage

Introduction Accurate identification of maize seed varieties is essential for enhancing crop yield and ensuring genetic purity in breeding programs. Methods This study establishes a non-destructive classification approach based on hyperspectral imaging for discriminating 30 widely cultivated maize varieties from Northwest China. Hyperspectral images were acquired within the 380–1018 nm range, and the embryo region of each seed was selected as the region of interest for spectral extraction. The collected spectra were preprocessed using Savitzky–Golay (SG) smoothing. Several machine learning models—KNN, ELM, and a two-layer convolutional neural network integrated with squeeze-and-excitation (SE) attention modules (CNN2c-SE)—were constructed and compared. Results Results demonstrated that the CNN2c-SE model utilizing full-spectrum data achieved a superior classification accuracy of 93.89%, significantly outperforming both conventional machine learning models and feature-waveband-based approaches. Discussion The proposed method offers an effective and efficient tool for high-throughput, non-destructive maize seed variety identification, with promising applications in seed quality control and precision breeding. 

pdf/se-enhanced-1-d-cnn-with-full-band-hyperspectral-imaging-for-h3pln47779wa.pdf

SE-enhanced 1-D CNN with full-band hyperspectral imaging for rapid and accurate maize seed variety classification

Accurate and efficient detection of defects in solar panels is essential for maintaining the stability and reliability of photovoltaic (PV) power generation systems. However, small defect sizes, complex backgrounds, and varying illumination conditions pose significant challenges to conventional detection methods. To address these issues, this study proposes RMN-YOLO, an enhanced detection model based on YOLOv11&#x2014;the latest real-time object detection framework that integrates multi-scale feature extraction and high-speed inference. RMN-YOLO introduces three key improvements: a Residual Spatial and Channel Reconstruction Convolution (RSCConv) module in the backbone to strengthen spatial&#x2013;channel feature representation; a Multi-Scale Edge Information Enhancement (MSEE) module in the neck to capture fine-grained defect boundaries; and a Dynamic Head (DyHead) with multi-attention mechanisms to improve small-object recognition and contextual awareness. Experiments on a publicly available solar panel defect dataset demonstrate that RMN-YOLO achieves a precision of 93.3%, recall of 82.7%, and mAP@50 of 90.5%, surpassing mainstream detectors such as SSD, Faster R-CNN, RetinaNet, and multiple YOLO variants. These results verify that RMN-YOLO effectively addresses the challenges of small-target detection and complex backgrounds, offering a practical and reliable solution for intelligent PV defect inspection. 

Accurate Detection of Solar Panel Defects Using RMN-YOLO With Multi-Scale Edge and Attention Enhancements

Visual Transformers (ViTs) have shown impressive performance due to their powerful coding ability to catch spatial and channel information. MetaFormer gives us a general architecture of transformers consisting of a token mixer and a channel mixer through which we can generally understand how transformers work. It is proved that the general architecture of the ViTs is more essential to the models’ performance than self-attention mechanism. Then, Depth-wise Convolution layer (DwConv) is widely accepted to replace local self-attention in transformers. In this work, a pure convolutional "transformer" is designed. We rethink the difference between the operation of self-attention and DwConv. It is found that the self-attention layer, with an embedding layer, unavoidably affects channel information, while DwConv only mixes the token information per channel. To address the differences between DwConv and self-attention, we implement DwConv with an embedding layer before as the token mixer to instantiate a MetaFormer block and a model named EmbedFormer is introduced. Meanwhile, SEBlock is applied in the channel mixer part to improve performance. On the ImageNet-1K classification task, EmbedFormer achieves top-1 accuracy of 81.7% without additional training images, surpassing the Swin transformer by +0.4% in similar complexity. In addition, EmbedFormer is evaluated in downstream tasks and the results are entirely above those of PoolFormer, ResNet and DeiT. Compared with PoolFormer-S24, another instance of MetaFormer, our EmbedFormer improves the score by +3.0% box AP/+2.3% mask AP on the COCO dataset and +1.3% mIoU on the ADE20K.

https://www.mdpi.com/1424-8220/22/24/9854/pdf?version=1671085357

EmbedFormer: Embedded Depth-Wise Convolution Layer for Token Mixing

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EmbedFormer: Embedded Depth-Wise Convolution Layer for Token Mixing