To enable the apple picking robot to quickly and accurately detect apples under the complex background in orchards, we propose an improved You Only Look Once version 4 (YOLOv4) model and data augmentation methods. Firstly, the crawler technology is utilized to collect pertinent apple images from the Internet for labeling. For the problem of insufficient image data caused by the random occlusion between leaves, in addition to traditional data augmentation techniques, a leaf illustration data augmentation method is proposed in this paper to accomplish data augmentation. Secondly, due to the large size and calculation of the YOLOv4 model, the backbone network Cross Stage Partial Darknet53 (CSPDarknet53) of the YOLOv4 model is replaced by EfficientNet, and convolution layer (Conv2D) is added to the three outputs to further adjust and extract the features, which make the model lighter and reduce the computational complexity. Finally, the apple detection experiment is performed on 2670 expanded samples. The test results show that the EfficientNet-B0-YOLOv4 model proposed in this paper has better detection performance than YOLOv3, YOLOv4, and Faster R-CNN with ResNet, which are state-of-the-art apple detection model. The average values of Recall, Precision, and F1 reach 97.43%, 95.52%, and 96.54% respectively, the average detection time per frame of the model is 0.338 s, which proves that the proposed method can be well applied in the vision system of picking robots in the apple industry.

Due to the spectral complexity and high dimensionality of hyperspectral images (HSIs), the processing of HSIs is susceptible to the curse of dimensionality. In addition, the classification results of ground truth are not ideal. To overcome the problem of the curse of dimensionality and improve classification accuracy, an improved spatial–spectral weight manifold embedding (ISS-WME) algorithm, which is based on hyperspectral data with their own manifold structure and local neighbors, is proposed in this study. The manifold structure was constructed using the structural weight matrix and the distance weight matrix. The structural weight matrix was composed of within-class and between-class coefficient representation matrices. These matrices were obtained by using the collaborative representation method. Furthermore, the distance weight matrix integrated the spatial and spectral information of HSIs. The ISS-WME algorithm describes the whole structure of the data by the weight matrix constructed by combining the within-class and between-class matrices and the spatial–spectral information of HSIs, and the nearest neighbor samples of the data are retained without changing when embedding to the low-dimensional space. To verify the classification effect of the ISS-WME algorithm, three classical data sets, namely Indian Pines, Pavia University, and Salinas scene, were subjected to experiments for this paper. Six methods of dimensionality reduction (DR) were used for comparison experiments using different classifiers such as k-nearest neighbor (KNN) and support vector machine (SVM). The experimental results show that the ISS-WME algorithm can represent the HSI structure better than other methods, and effectively improves the classification accuracy of HSIs.

This Letter suggests a novel method for improving the robustness and the geometric transformation modelling capability in multi‐view convolutional networks (MVCNNs). First, the deformable convolutional networks are used to learn more details and features related to the geometric transformation which the standard convolutional neural networks cannot handle. Then a view‐pooling layer is specifically designed for combining the descriptors from multiple views as the final representations of the 3D shapes. The key idea is to insert the deformable convolutional layer between the input and convolutional layer, making it possible to solve deformable 3D shape classification problems, which was a challenging task for MVCNN framework. The proposed method achieves state‐of‐the‐art classification results on two subsets of the ModelNet dataset (ModelNet10 and ModelNet40) over previous methods by a significant margin.

Category-level object 6D pose estimation is essential for robotic manipulation, augmented reality and 3D scene understanding. It aims to accurately predict the translation and rotation of arbitrary shape instances from a given set of object classes without models of each instance. However, such estimation is cumbersome owing to the intra-class variation and the difficulty of accurately regressing dense correspondences between an observed point cloud and the reconstructed instance model from high-order and complex features. In this study, we propose a framework comprising geometry-guided instance-aware prior and multi-stage reconstruction networks to address these challenges. The geometry-guided instance-aware prior network can avoid unstable RGB data interference and robustly learn implicit relations on semantic and geometric information between the observed point cloud and prior. We take advantage of these potential relations using a transformer network to cope with the intra-class variation. Furthermore, the proposed multi-stage reconstruction network is designed to learn the residual of the preliminary reconstruction and improve the accuracy of predicting dense correspondences originating from the complex feature. The results of extensive experiments on a well-acknowledged benchmark for category-level 6D pose estimation demonstrate that our proposed method achieves a significant improvement in performance compared to previous methods.

Multimodal 3D object detection significantly enhances perception by fusing LiDAR point clouds and RGB images. However, existing methods often fail to adaptively estimate modality confidence under challenging conditions such as heavy occlusion or sparse point clouds, leading to degraded fusion performance. In this paper, we propose GraphFusion, a multimodal framework that integrates cross-modal graph modeling with Bayesian uncertainty-aware fusion for robust 3D object detection. Specifically, a heterogeneous graph driven by geometric and semantic cues aligns 3D points with 2D pixels. A Bayesian attention mechanism then leverages predictive uncertainty to dynamically reweight modalities, prioritizing high-confidence information and enabling noise-resilient and spatially adaptive fusion. The proposed module is highly generalizable and can be seamlessly integrated into existing detectors as a plug-and-play component. Extensive experiments on KITTI and nuScenes demonstrate that GraphFusion achieves significant accuracy improvements with superior robustness and generalization, especially in complex environments.

Semantic segmentation from a three‐dimensional point cloud is vital in autonomous driving, computer vision, and augmented reality. However, current semantic segmentation does not effectively use the point cloud's local geometric features and contextual information, essential for improving segmentation accuracy. A semantic segmentation network that uses local feature fusion and a multilayer attention mechanism is proposed to address these challenges. Specifically, the authors designed a local feature fusion module to encode the geometric and feature information separately, which fully leverages the point cloud's feature perception and geometric structure representation. Furthermore, the authors designed a multilayer attention pooling module consisting of local attention pooling and cascade attention pooling to extract contextual information. Local attention pooling is used to learn local neighbourhood information, and cascade attention pooling captures contextual information from deeper local neighbourhoods. Finally, an enhanced feature representation of important information is obtained by aggregating the features from the two deep attention pooling methods. Extensive experiments on large‐scale point‐cloud datasets Stanford 3D large‐scale indoor spaces and SemanticKITTI indicate that authors network shows excellent advantages over existing representative methods regarding local geometric feature description and global contextual relationships.