Object detection is a crucial component of the perception system in autonomous driving. However, the road scene presents a highly intricate environment where the visibility and characteristics of traffic targets are susceptible to attenuation and loss due to various complex road scenarios such as lighting conditions, weather conditions, time of day, background elements, and traffic density. Nevertheless, the current object detection network must exhibit more learning capabilities when detecting such targets. This also exacerbates the loss of features during the feature extraction and fusion process, significantly compromising the network’s detection performance on traffic targets. This paper presents a novel methodology by which to overcome the concerns above, namely HRYNet. Firstly, a dual fusion gradual pyramid structure (DFGPN) is introduced, which employs a two-stage gradient fusion strategy to enhance the generation of more comprehensive multi-scale high-level semantic information, strengthen the interconnection between non-adjacent feature layers, and reduce the information gap that exists between them. HRYNet introduces an anti-interference feature extraction module, the residual multi-head self-attention mechanism (RMA). RMA enhances the target information by implementing a characteristic channel weighting policy, thereby reducing background interference and improving the attention capability of the network. Finally, the detection performance of HRYNet was evaluated by utilizing three datasets: the horizontally collected dataset BDD1000K, the UAV high-altitude dataset Visdrone, and a custom dataset. Experimental results demonstrate that HRYNet achieves a higher mAP_0.5 compared with YOLOv8s on the three datasets, with increases of 10.8%, 16.7%, and 5.5%, respectively. To optimize HRYNet for mobile devices, this study presents Lightweight HRYNet (LHRYNet), which effectively reduces the number of model parameters by 2 million. The results demonstrate that LHRYNet outperforms YOLOv8s in terms of mAP_0.5, with improvements of 6.7%, 10.9%, and 2.5% observed on the three datasets, respectively.

Walnut detection in mountainous and hilly regions often faces significant challenges due to obstructions, which adversely affect model performance. To address this issue, we collected a dataset comprising 2379 walnut images from these regions, with detailed annotations for both obstructed and non-obstructed walnuts. Based on this dataset, we propose OW-YOLO, a lightweight object detection model specifically designed for detecting small, obstructed walnuts. The model’s backbone was restructured with the integration of the DWR-DRB (Dilated Weighted Residual-Dilated Residual Block) module. To enhance efficiency and multi-scale feature fusion, we incorporated the HSFPN (High-Level Screening Feature Pyramid Network) and redesigned the detection head by replacing the original head with the more efficient LADH detection head while removing the head processing 32 × 32 feature maps. These improvements effectively reduced model complexity and significantly enhanced detection accuracy for obstructed walnuts. Experiments were conducted using the PyTorch framework on an NVIDIA GeForce RTX 4060 Ti GPU. The results demonstrate that OW-YOLO outperforms other models, achieving an mAP@0.5 (mean average precision) of 83.6%, mAP@[0.5:0.95] of 53.7%, and an F1 score of 77.9%. Additionally, the model’s parameter count decreased by 49.2%, weight file size was reduced by 48.1%, and computational load dropped by 37.3%, effectively mitigating the impact of obstruction on detection accuracy. These findings provide robust support for the future development of walnut agriculture and lay a solid foundation for the broader adoption of intelligent agriculture.

Object detection is one of the most critical areas in computer vision, and it plays an essential role in a variety of practice scenarios. However, small object detection has always been a key and difficult problem in the field of object detection. Therefore, considering the balance between the effectiveness and efficiency of the small object detection algorithm, this study proposes an improved YOLOX detection algorithm (BGD‐YOLOX) to improve the detection effect of small objects. We present the BigGhost module, which combines the Ghost model with a modulated deformable convolution to optimize the YOLOX for greater accuracy. At the same time, it can reduce the inference time by reducing the number of parameters and the amount of computation. The experimental results show that BGD‐YOLOX has a higher average accuracy rate in terms of small target detection, with mAP0.5 up to 88.3% and mAP0.95 up to 56.7%, which surpasses the most advanced object detection algorithms such as EfficientDet, CenterNet, and YOLOv4.

Introduction: Binocular color fusion and rivalry are two specific phenomena in binocular vision, which could be used as experimental tools to study how the brain processes conflicting information. There is a lack of objective evaluation indexes to distinguish the fusion or rivalry for dichoptic color.Methods: This paper introduced EEGNet to construct an EEG-based model for binocular color fusion and rivalry classification. We developed an EEG dataset from 10 subjects.Results: By dividing the EEG data from five different brain areas to train the corresponding models, experimental results showed that: (1) the brain area represented by the back area had a large difference on EEG signals, the accuracy of model reached the highest of 81.98%, and more channels decreased the model performance; (2) there was a large effect of inter-subject variability, and the EEG-based recognition is still a very challenge across subjects; and (3) the statistics of EEG data are relatively stationary at different time for the same individual, the EEG-based recognition is highly reproducible for an individual.Discussion: The critical channels for EEG-based binocular color fusion and rivalry could be meaningful for developing the brain computer interfaces (BCIs) based on color-related visual evoked potential (CVEP).

Accurate walnut yield prediction is crucial for the development of the walnut industry. Traditional manual counting methods are limited by labor and time costs, leading to inaccurate walnut quantity assessments. In this paper, we propose a walnut detection method based on UAV (UAV means Unmanned Aerial Vehicle) remote sensing imagery to improve the walnut yield prediction accuracy. Based on the YOLOv11 network, we propose several improvements to enhance the multi-scale object detection capability while achieving a more lightweight model structure. Specifically, we reconstruct the feature fusion network with a hierarchical scale-based feature pyramid structure and implement lightweight improvements to the feature extraction component. These modifications result in the RSWD-YOLO network (RSWD means remote sensing walnut detection; YOLO means ‘You Only Look Once’, and it is the specific abbreviation used for a series of object detection algorithms), which is specifically designed for walnut detection. Furthermore, to optimize the detection performance under hardware resource constraints, we apply knowledge distillation to RSWD-YOLO, thereby further improving the detection accuracy. Through model deployment and testing on small edge devices, we demonstrate the feasibility of our proposed method. The detection algorithm achieves 86.1% mean Average Precision on the walnut dataset while maintaining operational functionality on small edge devices. The experimental results demonstrate that our proposed UAV remote sensing-based walnut detection method has a significant practical application value and can provide valuable insights for future research in related fields.