Using deep learning-based object detection algorithms for landslide hazards detection is very popular and effective. However, most existing algorithms are designed for landslides in a specific geographical range. This paper constructs a set of landslide detection models YOLOX-Pro, based on the improved YOLOX (You Only Look Once) target detection model to address the poor detection of complex mixed landslides. Wherein the VariFocal is used to replace the binary cross entropy in the original classification loss function to solve the uneven distribution of landslide samples and improve the detection recall; the coordinate attention (CA) mechanism is added to enhance the detection accuracy. Firstly, 1200 historical landslide optical remote sensing images in thirty-eight areas of China were extracted from Google Earth to create a mixed sample set for landslide detection. Next, the three attention mechanisms were compared to form the YOLOX-Pro model. Then, we tested the performance of YOLOX-Pro by comparing it with four models: YOLOX, YOLOv5, Faster R-CNN, and Single Shot MultiBox Detector (SSD). The results show that the YOLOX-Pro(m) has significantly improved the detection accuracy of complex and small landslides than the other models, with an average precision (AP0.75) of 51.5%, APsmall of 36.50%, and ARsmall of 49.50%. In addition, optical remote sensing images of a 12.32 ${\mathrm{km}}^2$ group-occurring landslides area located in Mibei village, Longchuan County, Guangdong, China, and 750 Unmanned Aerial Vehicle (UAV) images collected from the Internet were also used for landslide detection. The research results proved that the proposed method has strong generalization and good detection performance for many types of landslides, which provide a technical reference for the broad application of landslide detection using UAV.

To address the current steel surface defect detection algorithms in practical applications involving low detection accuracy, an efficient and highly accurate strip steel surface defect detection algorithm, DEW-YOLO, is proposed in this paper. Firstly, by combining the advantages of deformable convolutional networks (DCNs), this paper innovates the C2F module in YOLOv8 and proposes a C2f_DCN module that can flexibly sample features to enhance the abilities of learning and expressing defect features of different sizes and shapes. Secondly, the explicit visual center (EVC) is introduced into the backbone network, which enhances feature extraction capabilities and adaptability and enables the model to better adjust features at different levels and scales. Finally, the original loss function is replaced with the Wise-IoU (WIoU) loss function to accurately measure the similarity between the target frames and improve the defect detection performance of the model. The experimental results on the NEU-DET dataset demonstrate that the algorithms proposed in this paper achieved a mean average precision (mAP) of 80.3% in steel surface defect detection tasks, which was a 3.9% improvement over the original YOLOv8 model. The model’s inference speed reached 91 frames per second (FPS). DEW-YOLO effectively enhances the accuracy of steel defect detection and better satisfies industrial inspection requirements.

Dragon fruit stem disease significantly affects both the quality and yield of dragon fruit. Therefore, there is an urgent need for an efficient, high-precision intelligent detection method to address the challenge of disease detection. To address the limitations of traditional methods, including slow detection and weak micro-integration capability, this paper proposes an improved YOLOv8-G algorithm. The algorithm reduces computational redundancy by introducing the C2f-Faster module. The loss function was modified to the structured intersection over union (SIoU), and the coordinate attention (CA) and content-aware reorganization feature extraction (CARAFE) modules were incorporated. These enhancements increased the model’s stability and improved its accuracy in recognizing small targets. Experimental results showed that the YOLOv8-G algorithm achieved a mean average precision (mAP) of 83.1% and mAP50:95 of 48.3%, representing improvements of 3.3% and 2.3%, respectively, compared to the original model. The model size and floating point operations per second (FLOPS) were reduced to 4.9 MB and 6.9 G, respectively, indicating reductions of 20% and 14.8%. The improved model achieves higher accuracy in disease detection while maintaining a lighter weight, serving as a valuable reference for researchers in the field of dragon fruit stem disease detection.

The scarcity of samples and disunity of feature inputs hinder the enhancement of transformer fault diagnosis performance, and there are mutual influences between model construction and feature selection, which cannot only consider a single process. Therefore, this study proposes a novel two‐stage transformer fault diagnosis strategy, which includes a multi‐filter interactive feature selection method (MIFS) constructed, and a diagnosis model ASSA‐SVM based on the adaptive sparrow algorithm (ASSA) optimised support vector machine (SVM). Firstly, the proposed MIFS incorporates ReliefF and mRMR to establish a comprehensive criterion ReliefF‐mRMR for feature importance ranking, and then performs dimension‐by‐dimension input classifier interaction selection based on the ranking results to obtain the optimal feature subset. Secondly, ASSA was proposed to optimise the kernel parameters of SVM. A two‐stage integration model MIFS‐ASSA‐SVM was developed. Finally, Experiments were conducted using real fault data, and the diagnostic performance of different feature inputs, optimisation algorithms and classifiers were compared. The results show that the proposed method performs well on parameter optimisation, can dynamically and interactively select feature subsets with few dimensions and good generalisation performance, its overall diagnosis accuracy reached 92.47%, and the diagnosis performance of each fault type has good performance in multiple evaluation metrics.

In complex environments, strawberry disease segmentation models face challenges, such as segmentation difficulties, excessive parameters, and high computational loads, making it difficult for these models to run effectively on devices with limited computational resources. To address the need for efficient running on low-power devices while ensuring effective disease segmentation in complex scenarios, this paper proposes BHI-YOLO, a lightweight instance segmentation model based on YOLOv8n-seg. First, the Universal Inverted Bottleneck (UIB) module is integrated into the backbone network and merged with the C2f module to create the C2f_UIB module; this approach reduces the parameter count while expanding the receptive field. Second, the HS-FPN is introduced to further reduce the parameter count and enhance the model’s ability to fuse features across different levels. Finally, by integrating the Inverted Residual Mobile Block (iRMB) with EMA to design the iRMA, the model is capable of efficiently combining global information to enhance local information. The experimental results demonstrate that the enhanced instance segmentation model for strawberry diseases achieved a mean average precision (mAP@50) of 93%. Compared to YOLOv8, which saw a 2.3% increase in mask mAP, the improved model reduced parameters by 47%, GFLOPs by 20%, and model size by 44.1%, achieving a relatively excellent lightweight effect. This study combines lightweight architecture with enhanced feature fusion, making the model more suitable for deployment on mobile devices, and provides a reference guide for strawberry disease segmentation applications in agricultural environments.

Driving safety has become one of the top concerns of people in contemporary society, with regular tire inspection being indispensable to ensure safe driving. However, traditional methods of tire defect detection have encountered problems such as slow detection speed, complex tire defect backgrounds, and limited hardware resources. To address the above problems, this paper proposes a lightweight YOLOv8n-SOI algorithm for tire defect detection. First, a similarity-based attention mechanism (SimAM) was introduced to the C2f block of the backbone network to improve the ability to extract the shape features of irregular tire defects in complicated backdrops. Subsequently, four network structures were created in response to the need for lightweighting the detection procedure by incorporating the omni-dimensional dynamic convolution (ODConv) network structure at various positions. The network structure with the highest detection accuracy and smaller model parameters was chosen. Finally, Inner-intersection over union (Inner-IOU), an auxiliary edge loss function that concentrated more on the centre of the target, was added to the modified network to speed up tire defect regression convergence. Experimental results demonstrated that YOLOv8n-SOI surpassed the YOLOv8n algorithm regarding precision, recall, and mean average precision (mAP) by 3.8%, 1.1%, and 3%, respectively. Additionally, YOLOv8n-SOI reduced floating-point operations (FLOPs) by 6% and had a model size of only 6.5MB. This article presents the latest you only look once (YOLO) model for tire identification, contributing to the advancement of research in tire defect detection and providing a level of assurance for driving safety.