Study on Optimized Elman Neural Network Classification Algorithm Based on PLS and CA

High-dimensional large sample data sets, between feature variables and between samples, may cause some correlative or repetitive factors, occupy lots of storage space, and consume much computing time. Using the Elman neural network to deal with them, too many inputs will influence the operating efficiency and recognition accuracy; too many simultaneous training samples, as well as being not able to get precise neural network model, also restrict the recognition accuracy. Aiming at these series of problems, we introduce the partial least squares (PLS) and cluster analysis (CA) into Elman neural network algorithm, by the PLS for dimension reduction which can eliminate the correlative and repetitive factors of the features. Using CA eliminates the correlative and repetitive factors of the sample. If some subclass becomes small sample, with high-dimensional feature and fewer numbers, PLS shows a unique advantage. Each subclass is regarded as one training sample to train the different precise neural network models. Then simulation samples are discriminated and classified into different subclasses, using the corresponding neural network to recognize it. An optimized Elman neural network classification algorithm based on PLS and CA (PLS-CA-Elman algorithm) is established. The new algorithm aims at improving the operating efficiency and recognition accuracy. By the case analysis, the new algorithm has unique superiority, worthy of further promotion

DLNet: Accurate segmentation of green fruit in obscured environments

o achieve more accurate recognition and segmentation of obscured fruit in natural orchard environments, DLNet model is proposed. The model is improved for the more challenging problem of segmenting overlapping fruit from homochromatic backgrounds without considering various damages. This approach is tantamount to construct the detection network RS-RFP and the segmentation network DLNet. RS-RFP extends Full Convolutional One-Stage Object Detection (FCOS). Specifically, Feature Pyramid Network (FPN) by adding Gaussian non-local attention mechanism to build Refined Pyramid Network (RFP) for refining semantic features generated continuously by Residual Network (ResNet) and FPN. The DLNet segmentation framework is composed of a dual-layer Graph Attention Networks (GAT) layer is constructed to model the image as two overlapping layers, where the top GAT layer detects the occluded object (occluded) and the bottom GAT layer infers the partially occluded instance (occlude). Display modeling of the two-layer structure occlusion relationship can naturally the boundaries between the occluded and occlude instances and consider their interactions. The experimental results show that the method outperforms earlier segmentation models and achieves metric values of 80.9% and 81.2% for Average Precision (AP) box and AP mask respectively. In a reasonable running time, it meets the requirements of accuracy and robustness for picking robots and provides a reference for segmentation of other fruits and vegetables

An Optimized Classification Algorithm by Neural Network Ensemble Based on PLS and OLS

Using the neural network to classify the data which has higher dimension and fewer samples means overmuch feature inputs influence the structure design of neural network and fewer samples will generate incomplete or overfitting phenomenon during the neural network training. All of the above will restrict the recognition precision obviously. It is even better to use neural network to classify and, therefore, propose a neural network ensemble optimized classification algorithm based on PLS and OLS in this paper. The new algorithm takes some advantages of partial least squares (PLS) algorithm to reduce the feature dimension of small sample data, which obtains the low-dimensional and stronger illustrative data; using ordinary least squares (OLS) theory determines the weights of each neural network in ensemble learning system. Feature dimension reduction is applied to simplify the neural network’s structure and improve the operation efficiency; ensemble learning can compensate for the information loss caused by the dimension reduction; on the other hand, it improves the recognition precision of classification system. Finally, through the case analysis, the experiment results suggest that the operating efficiency and recognition precision of new algorithm are greatly improved, which is worthy of further promotion

RS-Net: robust segmentation of green overlapped apples

Fruit detection and segmentation will be essential for future agronomic management, with applications in yield estimation, growth monitoring, intelligent picking, disease detection and etc. In order to more accurately and efficiently realize the recognition and segmentation of apples in natural orchards, a robust segmentation net framework specially developed for fruit production is proposed. This model was improved for the more challenging problem which segments the overlapped apples from the monochromatic background regardless of various corruptions. The method extends Mask R-CNN by embedding an attention mechanism for focusing more on the informative pixels but also suppressing the noise caused by adverse factors (occlusions, overlaps, etc.), which could be more suitable and robust for operating in complex natural environment. Specifically, the Gaussian non-local attention mechanism is transplanted into Mask R-CNN for refining the semantic features generated continuously by residual network and feature pyramid network, then the model forward processing based on the balanced feature levels and finally segments the regions where the apples are located. Experimental results verify the hypothesis of current work and show that the proposed method outperforms other start-of-the-art detection and segmentation models, the AP box and AP mask metric values have reached 85.6% and 86.2% in a reasonable run time, respectively, which can meet the precision and robustness of vision system in agronomic managemen

FCOS-LSC: A novel model for green fruit detection in a complex orchard environment

To better address the difficulties in designing green fruit recognition techniques in machine vision systems, we propose an optimized FCOS (full convolutional one-stage object detection) algorithm based on LSC attention blocks (FCOS-LSC) that are performed on level scales, spaces and channels of feature map. The method achieves efficient recognition and localization of green fruit images affected by overlapping occlusions, lighting conditions and capture angles. Specifically, the improved feature extraction network ResNet50 with added deformable convolution is used to fully extract green fruit feature information. The feature pyramid network (FPN) is employed to fully fuse low-level detail information and high-level semantic information in a cross-connected and top-down connected way. Next, the attention mechanisms are added to each of the three dimensions of scale, space (including the height and width of the feature map) and channel of the generated multi-scale feature map to improve the feature perception capability of the network. Finally, the classification and regression sub-networks of the model are applied to predict the fruit category and bounding box. In the classification branch, a new positive and negative sample selection strategy is applied to better distinguish supervised signals by designing weights in the loss function to achieve more accurate fruit detection. The proposed FCOS-LSC model has 38.65M parameters (Params), 38.72G floating point operations (FLOPs), and mean average precision (mAP) of 63.0% and 75.2% for detecting green apples and green persimmons, respectively. In summary, FCOS-LSC outperforms the state-of-the-art models in terms of precision and complexity to meet the accurate and efficient requirements of green fruit recognition by intelligent agricultural equipment. Correspondingly, FCOS-LSC can be used to improve the robustness and generalization of the green fruit detection models

An accurate green fruits detection method based on optimized YOLOX-m

Fruit detection and recognition has an important impact on fruit and vegetable harvesting, yield prediction and growth information monitoring in the automation process of modern agriculture, and the actual complex environment of orchards poses some challenges for accurate fruit detection. In order to achieve accurate detection of green fruits in complex orchard environments, this paper proposes an accurate object detection method for green fruits based on optimized YOLOX_m. First, the model extracts features from the input image using the CSPDarkNet backbone network to obtain three effective feature layers at different scales. Then, these effective feature layers are fed into the feature fusion pyramid network for enhanced feature extraction, which combines feature information from different scales, and in this process, the Atrous spatial pyramid pooling (ASPP) module is used to increase the receptive field and enhance the network’s ability to obtain multi-scale contextual information. Finally, the fused features are fed into the head prediction network for classification prediction and regression prediction. In addition, Varifocal loss is used to mitigate the negative impact of unbalanced distribution of positive and negative samples to obtain higher precision. The experimental results show that the model in this paper has improved on both apple and persimmon datasets, with the average precision (AP) reaching 64.3% and 74.7%, respectively. Compared with other models commonly used for detection, the model approach in this study has a higher average precision and has improved in other performance metrics, which can provide a reference for the detection of other fruits and vegetables

Rapid Non-Destructive Analysis of Food Nutrient Content Using Swin-Nutrition

Food non-destructive detection technology (NDDT) is a powerful impetus to the development of food safety and quality. One of the essential tasks of food quality regulation is the non-destructive detection of the food’s nutrient content. However, existing food nutrient NDDT performs poorly in terms of efficiency and accuracy, which hinders their widespread application in daily meals. Therefore, this paper proposed an end-to-end food nutrition non-destructive detection method, named Swin-Nutrition, which combined deep learning and NDDT to evaluate the nutrient content of food. The method aimed to fully capture the feature information from the food images and thus accurately estimate the nutrient content. Swin-Nutrition resorted to Swin Transformer, the feature fusion module (FFM), and the nutrient prediction module to evaluate nutrient content. In particular, Swin Transformer acted as the backbone network for feature extraction of food images, and FFM was used to obtain the discriminative feature representation to improve the accuracy of prediction. The experimental results on the Nutrition5k dataset demonstrated the effectiveness and efficiency of our proposed method. Specifically, the mean value of the percentage mean absolute error (PMAE) for calories, mass, fat, carbohydrate, and protein were only 15.3%, 12.5%, 22.1%, 20.8%, and 15.4%, respectively. We hope that our simple and effective method will provide a solid foundation for the research of food NDDT

Symmetric Deformable Registration via Learning a Pseudomean for MR Brain Images

Image registration is a fundamental task in medical imaging analysis, which is commonly used during image-guided interventions and data fusion. In this paper, we present a deep learning architecture to symmetrically learn and predict the deformation field between a pair of images in an unsupervised fashion. To achieve this, we design a deep regression network to predict a deformation field that can be used to align the template-subject image pair. Specifically, instead of estimating the single deformation pathway to align the images, herein, we predict two halfway deformations, which can move the original template and subject into a pseudomean space simultaneously. Therefore, we train a symmetric registration network (S-Net) in this paper. By using a symmetric strategy, the registration can be more accurate and robust particularly on the images with large anatomical variations. Moreover, the smoothness of the deformation is also significantly improved. Experimental results have demonstrated that the trained model can directly predict the symmetric deformations on new image pairs from different databases, consistently producing accurate and robust registration results

Mask Positioner: An effective segmentation algorithm for green fruit in complex environment

In order to enable intelligent orchard management and the application of harvesting robots, it is necessary to improve the accuracy of computer vision technology for green fruit segmentation in complex orchard environments. However, existing segmentation algorithms are unable to generate precise fruit masks in such environments. This paper proposes a novel and efficient segmentation algorithm called Mask Positioner for accurate fruit segmentation. The Mask Positioner applies a layer-by-layer filtering approach to refine feature maps generated by the detail refinement network, resulting in a refined mask. The selected pixels are then input to the order decoder to determine their relevance to the fruit region. Finally, the determined pixels are used to generate the final mask, resulting in accurate and efficient fruit segmentation. Mask Positioner is verified by a green persimmon dataset made for the complex background. The experimental results show that the segmentation accuracy of Mask Positioner reaches 67.4%, and the detection accuracy reaches 69.1%. For small fruits, its detection and segmentation accuracy are at least 1.0 and 3.2 percentage points higher than other algorithms. Additionally, the generalization ability of the algorithm is verified using a green apple dataset. Experiments show that it does well in the green fruit segmentation

Few‐shot logo detection

Abstract The proliferation of deep learning has driven research into deep learning‐based logo detection, which usually needs a large number of annotated data to train the model. However, due to the occasional appearance of new brands or the high cost of annotation, the number of training data is limited. Against this backdrop, the authors adapt the few‐shot object detection into logo detection, and thus present a cutting‐edge method called Double Classification Head (DCH) for Few‐Shot Logo Detection (DCH‐FSLogo), which aims at detecting the unseen logo classes using few annotated data. Unlike the traditional few‐shot detection, some logo objects are similar to their backgrounds and have diverse shapes as well. For this reason, the authors adopt balanced feature pyramid and deformable Region of Interest pooling in DCH‐FSLogo, this enhances the feature extraction capability and adapts to the different logo shapes. In addition, we introduce the DCH for few‐shot logo detection to detect logo objects using few annotated data. Specifically, we use an extra classification head for the base classes to ease the influence from the novel classes. The experimental results on four datasets, namely: FlickrLogos‐32, FoodLogoDet‐1500‐100, LogoDet‐3K‐100 and QMUL‐OpenLogo‐100, demonstrate that our method achieves better performance