Bin Guo

Apple watercore is an internal physiological disorder that affects fruit quality and storage stability. This study developed a non-destructive approach for classifying watercore severity in 737 Aksu ‘Fuji’ apples using near-infrared hyperspectral imaging (930–1720 nm). Watercore severity was quantified using the watercore severity index (WSI) derived from Fiji-based segmentation of equatorial cross-sectional images, and samples were classified into sound, slight, moderate, and severe classes according to published criteria. After spectral preprocessing, support vector machine (SVM), random forest (RF), baseline one-dimensional convolutional neural network (1D-CNN), and attention-enhanced 1D-CNN-CBAM-SE models were evaluated using five repeated stratified holdout experiments. SVM and 1D-CNN-CBAM-SE achieved comparable performance, with SVM obtaining an accuracy of 98.74% and a Macro-F1 score of 98.86%, and 1D-CNN-CBAM-SE achieving the same accuracy and a Macro-F1 score of 98.85%. Ablation experiments showed that attention modules improved the baseline 1D-CNN, with CBAM providing the major performance gain and the addition of SE further reducing performance variability across repeated experiments. Misclassifications were largely confined to adjacent severity classes. These results support the feasibility of NIR-HSI combined with spectral classification models for non-destructive watercore severity grading.

Introduction: Deep learning-based apple maturity detection supports precise management in smart agriculture. However, deployment on resource-constrained edge devices requires minimizing network weights while ensuring accuracy, a challenge compounded by inclement weather and dense fruit clustering in orchard environments. Methods: To address these challenges, we propose HRLN-YOLO, a lightweight high-efficiency apple maturity detection model that minimizes network weights while ensuring accuracy. Specifically, we design a lightweight backbone HGBackbone to enhance feature extraction and accelerate inference, construct an enhanced neck module RCF_Neck to improve multi-scale feature fusion under occlusion, develop a lightweight detection head LADH-Head to alleviate task conflicts with minimal computational cost, and introduce NWD-Loss to improve localization stability for small-scale targets. Results: Experiments on the Orchard Apple Maturity Dataset demonstrate that HRLN-YOLO improves mAP@0.5 by 1.7% over the YOLO11n baseline while reducing parameters by 37.3% and computational complexity by 34.9%. Discussion: The core contribution of this study lies in minimizing network weights while ensuring detection accuracy, providing a practical solution for edge deployment in smart orchard automated harvesting.

Urban underground sewage pipelines often suffer from defects such as cracks, irregular joint misalignment, and stratified sedimentation blockages, which may lead to pipeline bursts, sewage overflow, and water pollution. Timely detection of abnormal defects in sewage pipelines is critical to ensuring public health and environmental sustainability. Vision-based sewage pipeline defect detection plays a crucial role in modern urban wastewater treatment systems. However, it still faces challenges such as limited feature extraction capabilities, insufficient multi-scale defect characterization, and poor positioning stability when dealing with low-contrast images and in environments with severe background interference. To address this issue, this study proposes an enhanced SAW-YOLOv8l model that integrates RT-DETR (real-time detection Transformer) with CNN (convolutional neural network) architecture. First, a C2f_SCA module improves the long-distance feature extraction capability and localization precision. Second, an AIFI-PRBN module enhances global feature correlation through attention-mechanism-based intra-scale feature interaction and reduces computational complexity using lightweight techniques. Finally, an adaptive dynamic weighted loss function based on Wise-IoU (weighted intersection over union) further improves training convergence and robustness by balancing the gradient distribution of samples. Experiments on a mixed dataset comprising Sewer-ML and industrial images demonstrate that the SAW-YOLOv8l model achieved mAP@0.5 of 86.2% and precision of 84.4%, which were improvements of 2.4% and 6.6% respectively over the baseline model, significantly enhancing the detection performance of abnormal defects in sewage pipelines.

To address the degradation in communication performance caused by the high mobility and dynamic uncertainty in vehicular network channels, this paper proposes a hybrid resource allocation framework that integrates the advantage actor–critic (A2C) algorithm with the multi-agent deep deterministic policy gradient (MADDPG) algorithm. By modeling the high-speed vehicular network environment, the resource allocation task is formulated as a multi-agent deep reinforcement learning (MADRL) problem within a continuous action space. The proposed framework leverages the advantage function to refine gradient estimation, thereby improving training stability and convergence behavior. Additionally, regularization penalty terms and constraint mechanisms are incorporated into the learning process to balance multiple communication objectives. Specifically, the method aims to maximize the throughput of vehicle-to-infrastructure (V2I) links while ensuring the transmission reliability of vehicle-to-vehicle (V2V) links. In simulation experiments, the proposed method performs better in terms of convergence. Compared with the conventional MADDPG algorithm, the average access success probability is improved by 1.6%, and the average V2I throughput increases by 3.5%, indicating a significant enhancement in overall vehicular communication efficiency and transmission performance.

Detecting small objects in unmanned aerial vehicle (UAV) imagery remains a challenging task due to the limited target scale, cluttered backgrounds, severe occlusion, and motion blur commonly observed in dynamic aerial environments. This study presents STAIR-DETR, a real-time synergistic detection framework derived from RT-DETR, featuring comprehensive enhancements in feature extraction, resolution transformation, and detection head design. A Statistical Feature Attention (SFA) module is incorporated into the neck to replace the original AIFI, enabling token-level statistical modeling that strengthens fine-grained feature representation while effectively suppressing background interference. The backbone is reinforced with a Diverse Semantic Enhancement Block (DSEB), which employs multi-branch pathways and dynamic convolution to enrich semantic expressiveness without sacrificing spatial precision. To mitigate information loss during scale transformation, an Adaptive Scale Transformation Operator (ASTO) is proposed by integrating Context-Guided Downsampling (CGD) and Dynamic Sampling (DySample), achieving context-aware compression and content-adaptive reconstruction across resolutions. In addition, a high-resolution P2 detection head is introduced to leverage shallow-layer features for accurate classification and localization of extremely small targets. Extensive experiments conducted on the VisDrone2019 dataset demonstrate that STAIR-DETR attains 41.7% mAP@50 and 23.4% mAP@50:95, outperforming contemporary state-of-the-art (SOTA) detectors while maintaining real-time inference efficiency. These results confirm the effectiveness and robustness of STAIR-DETR for precise small object detection in complex UAV-based imaging scenarios.

Horse iris recognition is a non-invasive identification method with great potential for precise management in intelligent horse farms. However, horses’ natural vigilance often leads to stress and resistance when exposed to close-range infrared cameras. This behavior makes it challenging to capture clear iris images, thereby reducing recognition performance. This paper addresses the challenge of generating high-resolution iris images from existing low-resolution counterparts. To this end, we propose a novel hybrid-architecture image super-resolution (SR) network. Central to our approach is the design of Paired Asymmetric Transformer Block (PATB), which incorporates Contextual Query Generator (CQG) to efficiently capture contextual information and model global feature interactions. Furthermore, we introduce an Efficient Residual Dense Block (ERDB), specifically engineered to effectively extract finer-grained local features inherent in the image data. By integrating PATB and ERDB, our network achieves superior fusion of global contextual awareness and local detail information, thereby significantly enhancing the reconstruction quality of horse iris images. Experimental evaluations on our self-constructed dataset of horse irises demonstrate the effectiveness of the proposed method. In terms of standard image quality metrics, it achieves the PSNR of 30.5988 dB and SSIM of 0.8552. Moreover, in terms of identity-recognition performance, the method achieves Precision, Recall, and F1-Score of 81.48%, 74.38%, and 77.77%, respectively. This study provides a useful contribution to digital horse farm management and supports the ongoing development of smart animal husbandry.

Although deep learning has significantly advanced brain tumor MRI segmentation and preoperative planning, existing methods like U-Net and Transformer, which are widely used Encoder–Decoder architectures in medical image segmentation, still have limitations. Specifically, these methods fail to fully leverage the unique characteristics of different MRI modalities during the feature extraction stage, thereby hindering further improvements in segmentation accuracy. Currently, MRI modalities are typically treated as independent entities or as uncorrelated features during feature extraction, neglecting their potential interdependencies. To address this gap, we introduce the GMD architecture (Grouped Encoder-Mixer-Decoder), which is designed to enhance information capture during feature extraction by considering the intercorrelation and complementary nature of different modalities. In the proposed GMD architecture, input images are first grouped by modality in the grouped encoder based on a modality-specific strategy. The extracted features are then fused and optimized in the mixer module, and the final segmentation is achieved through the decoder. We implement this architecture in GMDNet to validate its effectiveness. Experiments demonstrate that GMDNet not only achieves outstanding performance under complete modality conditions but also maintains robust performance even when certain modalities are missing. To further enhance performance in incomplete modality, we propose an innovative reuse modality strategy that significantly improves segmentation accuracy compared to conventional approaches. We evaluated the performance of GMDNet on the BraTS 2018 and BraTS 2021 datasets. Under complete modality conditions, GMDNet achieved Dice scores of 91.21%, 87.11%, 80.97%, and 86.43% for WT (Whole Tumor), TC (Tumor Core), ET (Enhancing Tumor) and Average on the BraTS 2018, and 91.87%, 87.25%, 83.16%, and 87.42% on the BraTS 2021. Under incomplete modality conditions, when T1, T1ce, T2, and FLAIR were missing, the Dice scores on the BraTS 2021 dataset were 86.47%, 73.29%, 86.46%, and 82.54%, respectively. After applying the reuse modality strategy, the scores improved to 87.17%, 75.07%, 86.91%, and 86.22%. Overall, extensive experiments demonstrate that proposed GMDNet architecture achieves state-of-the-art performance, outperforming the compared models of this paper in complete or incomplete modality.

Brain tumors are a type of disease that affects people’s health and have received extensive attention. Accurate segmentation of Magnetic Resonance Imaging (MRI) images for brain tumors is essential for effective treatment strategies. However, there is scope for enhancing the segmentation accuracy of established deep learning approaches, such as 3D U-Net. In pursuit of improved segmentation precision for brain tumor MRI images, we propose the MEASegNet, which incorporates multiple efficient attention mechanisms into the 3D U-Net architecture. The encoder employs Parallel Channel and Spatial Attention Block (PCSAB), the bottleneck layer leverages Channel Reduce Residual Atrous Spatial Pyramid Pooling (CRRASPP) attention, and the decoder layer incorporates Selective Large Receptive Field Block (SLRFB). Through the integration of various attention mechanisms, we enhance the capacity for detailed feature extraction, facilitate the interplay among distinct features, and ensure the retention of more comprehensive feature information. Consequently, this leads to an enhancement in the segmentation precision of 3D U-Net for brain tumor MRI images. In conclusion, our extensive experimentation on the BraTS2021 dataset yields Dice scores of 92.50%, 87.49%, and 84.16% for Whole Tumor (WT), Tumor Core (TC), and Enhancing Tumor (ET), respectively. These results indicate a marked improvement in segmentation accuracy over the conventional 3D U-Net.

Brain tumors are serious conditions, which can cause great trauma to patients, endangering their health and even leading to disability or death. Therefore, accurate preoperative diagnosis is particularly important. Accurate brain tumor segmentation based on deep learning plays an important role in the preoperative treatment planning process and has achieved good performance. However, one of the challenges involved is an insufficient ability to extract features with a large receptive field in encoder layers and guide the selection of deep semantic information in decoder layers. We propose small kernel convolution with an effective receptive field network (SCENet) based on UNet, which involves a small kernel convolution with effective receptive field shuffle module (SCER) and a channel spatial attention module (CSAM). The SCER module utilizes the inherent properties of stacking convolution to obtain effectively receptive fields and improve the features with a large receptive field extraction ability. CSAM of decoder layers can preserve more detailed features to capture clearer contours of the segmented image by calculating the weights of channels and spaces. An ASPP module is introduced to the bottleneck layer to enlarge the receptive field and can capture multi-scale detailed features. Furthermore, a large number of experiments were performed to evaluate the performance of our model on the BraTS2021 dataset. The SCENet achieved dice coefficient scores of 91.67%, 87.70%, and 83.35% for whole tumor (WT), tumor core (TC), and enhancing tumor (ET), respectively. The results show that the proposed model achieves the state-of-the-art performance compared with more than twelve benchmarks.