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Arbitrary-oriented ship detection has become challenging due to problems of high resolution, poor imaging clarity, and large size differences between targets in remote sensing images. Most of the existing ship detection methods are difficult to use simultaneously to meet the requirements of high accuracy and speed. Therefore, we designed a lightweight and efficient multi-scale feature dilated neck module in the YOLO11 network to achieve the high-precision detection of arbitrary-oriented ships in remote sensing images. Firstly, multi-scale dilated attention is utilized to effectively capture the multi-scale semantic details of ships in remote sensing images. Secondly, the interaction between the spatial information of remote sensing images and the semantic information of low-resolution features of ships is realized by using the cross-stage partial stage. Finally, the GSConv module is introduced to minimize the loss of semantic information on ship features during transmission. The experimental results show that the proposed method has the advantages of light structure and high accuracy, and the ship detection performance is better than the state-of-the-art detection methods. Compared with YOLO11n, it improves 3.1% of mAP@0.5 and 3.3% of mAP@0.5:0.95 on the HRSC2016 dataset and 1.9% of mAP@0.5 and 1.3% of mAP@0.5:0.95 on the MMShip dataset.

Compared with conventional targets, small objects often face challenges such as smaller size, lower resolution, weaker contrast, and more background interference, making their detection more difficult. To address this issue, this paper proposes an improved small object detection method based on the YOLO11 model—PC-YOLO11s. The core innovation of PC-YOLO11s lies in the optimization of the detection network structure, which includes the following aspects: Firstly, PC-YOLO11s has adjusted the hierarchical structure of the detection network and added a P2 layer specifically for small object detection. By extracting the feature information of small objects in the high-resolution stage of the image, the P2 layer helps the network better capture small objects. At the same time, in order to reduce unnecessary calculations and lower the complexity of the model, we removed the P5 layer. In addition, we have introduced the coordinate spatial attention mechanism, which can help the network more accurately obtain the spatial and positional features required for small targets, thereby further improving detection accuracy. In the VisDrone2019 datasets, experimental results show that PC-YOLO11s outperforms other existing YOLO-series models in overall performance. Compared with the baseline YOLO11s model, PC-YOLO11s mAP@0.5 increased from 39.5% to 43.8%, mAP@0.5:0.95 increased from 23.6% to 26.3%, and the parameter count decreased from 9.416M to 7.103M. Not only that, we also applied PC-YOLO11s to tea bud datasets, and experiments showed that its performance is superior to other YOLO-series models. Experiments have shown that PC-YOLO11s exhibits excellent performance in small object detection tasks, with strong accuracy improvement and good generalization ability, which can meet the needs of small object detection in practical applications.

Manual monitoring of plastic film and drip tape laying quality during mulch-covered seed drill operations is limited by high labor intensity, delayed fault detection, and unstable accuracy. To solve these problems, this study proposes a multi-source data acquisition method combining depth cameras and inductive proximity switches, and develops a dedicated monitoring system with a Raspberry Pi 5 as the core controller. Aiming at the key challenges of accurate mulch film segmentation and limited computing power of edge devices, an enhanced lightweight YOLOv11n-seg-DBM segmentation model is proposed, it integrates a C3k2-DWR module to enhance multi-scale feature extraction, a C2BRA module to optimize cross-layer feature interaction, an MFM module for dynamic feature fusion, and a Focaler-CIoU loss function to improve hard sample mining. After pruning and quantization, the model is successfully deployed on edge devices. The system achieves quantitative detection of mulch exposed area and soil coverage width via sub-pixel edge detection and depth information calculation, and realizes real-time fault diagnosis and alarm for film and tape breakage through proximity switch signal sequence analysis. Ablation test results show the improved model outperforms the baseline by 3.52 percentage points in mIoU and 3.35 percentage points in MPA, with parameters reduced to 2.75M Field trials confirm average monitoring accuracies of 93.05% for mulch exposed area and 93.02% for soil coverage width, with average response times of 1.21 s for film breakage and 1.59 s for tape breakage. This study provides an effective technical solution for intelligent operation monitoring and quality improvement of mulch-covered seed drills.

This study presents SkinLearn, a YOLO11-powered deep learning-based smartphone application (app) designed for real-time skin disease classification using images captured by smartphone cameras. The proposed system addresses critical challenges in mobile-based dermatological diagnosis, such as the diversity of skin conditions, varying image qualities, and computational constraints. By leveraging the enhanced architecture of YOLO11, SkinLearn achieves superior accuracy and speed compared to existing models, making it suitable for on-device deployment in low-resource settings. The model was trained using an open-source skin disease dataset and demonstrated robust performance across multiple disease classes. The YOLO11 model exhibited a 98.3% classification accuracy for skin diseases. The results validate the potential of the system as a practical diagnostic aid for early skin disease identification and clinical decision support. This study contributes to the growing sector of AI-driven mobile healthcare by offering an accessible and scalable solution for dermatological screening.

Accurate real-time detection of small traffic objects remains a critical challenge for onboard vision-based traffic perception, particularly under conditions of weak texture, scale variation, occlusion, and limited computational resources. To address these challenges, this paper proposes DER-YOLO, a lightweight small-object-oriented detector built upon YOLO11n, specifically designed for complex traffic scenes. DER-YOLO introduces stage-wise feature calibration across the backbone, neck, and pre-head stages to enhance small-object representation. First, a Decoupled Global Context C3k2 (DGC-C3k2) module strengthens contextual representation for weak and low-saliency traffic objects after local feature extraction. Second, an ECA-guided Cross-scale Adaptive Fusion (ECAF) module adaptively balances high-level semantic cues and shallow high-resolution details to improve multi-scale feature interaction. Third, a Refined Large Selective Kernel (RLSK) module refines high-resolution spatial responses before the P3 detection head, enhancing small-object localization. Extensive experiments on KITTI and BDD100K demonstrate that DER-YOLO improves detection accuracy while maintaining real-time inference. On KITTI, it achieves 86.95% mAP@0.5 and 60.89% mAP@0.5:0.95, with small-object AP@0.5:0.95 increasing from 27.0% to 29.3%. On BDD100K, it achieves 55.05% mAP@0.5 and 29.21% mAP@0.5:0.95, with small-object AP@0.5:0.95 increasing from 12.6% to 15.2%. With 2.744 M parameters, 7.401 GFLOPs, and over 100 FPS, DER-YOLO provides an effective and lightweight solution for real-time small-object detection in onboard traffic perception scenarios.

With the development of the railway industry and the progression of deep learning technology, object detection algorithms have been gradually applied to track defect detection. To address the issues of low detection efficiency and inadequate accuracy, we developed an improved orbital defect detection algorithm utilizing the YOLO11 model. First, the conventional convolutional layers in the YOLO (You Only Look Once) 11backbone network were substituted with the SPD-Conv (Spatial Pyramid Dilation Convolution) module to enhance the model’s detection performance on low-resolution images and small objects. Secondly, the EffectiveSE (Effective Squeeze-Excitation) attention mechanism was integrated into the backbone network to enhance the model’s utilization of feature information across various layers, thereby improving its feature representation capability. Finally, a small target detection head was added to the neck network to capture targets of different scales. These improvements help the model identify targets in more difficult tasks and ensure that the neural network allocates more attention to each target instance, thus improving the model’s performance and accuracy. In order to verify the effectiveness of this model in track defect detection tasks, we created a track fastener dataset and a track surface dataset and conducted experiments. The mean Average Precision (mAP@0.5) of the improved algorithm on track fastener dataset and track surface dataset reached 95.9% and 89.5%, respectively, which not only surpasses the original YOLO11 model but also outperforms other widely used object detection algorithms. Our method effectively improves the efficiency and accuracy of track defect detection.

To address the issues of low detection accuracy and poor adaptability in complex orchard environments (such as varying lighting conditions, branch and leaf occlusion, fruit overlap, and small targets), this paper proposes an improved pear detection model based on YOLO11, called YOLO11-Pear. First, to improve the model’s capability in detecting occluded pears, the C2PSS module is introduced to replace the original C2PSA module. Second, a small target detection layer is added to improve the model’s ability to detect small pears. Finally, the upsampling process is replaced with DySample, which not only maintains a high efficiency but also improves the processing speed and expands the model’s application range. To validate the effectiveness of the model, a dataset of images of Qiu Yue pears and Cui Guan pears was constructed. The experimental results showed that the improved YOLO11-Pear model achieved precision, recall, mAP50, and mAP50–95 values of 96.3%, 84.2%, 92.1%, and 80.2%, respectively, outperforming YOLO11n by 3.6%, 1%, 2.1%, and 3.2%. With only a 2.4% increase in the number of parameters compared to the original model, YOLO11-Pear enables fast and accurate pear detection in complex orchard environments.

Optical remote sensing object detection faces challenges such as large variations in scale, slender and direction-sensitive targets, complex backgrounds, and limited deployment resources. This paper proposes a lightweight geometrically decoupled student network with a dynamic multi-teacher distillation framework. Based on YOLO11n, the student detector keeps the original classification branch while redesigning the regression branches at different scales. Lightweight regression towers are used for the shallow and deep branches, whereas a geometrically decoupled regression tower is introduced only at the intermediate branch to enhance localization for slender and orientation-sensitive objects with limited extra cost. A geometry-adaptive box loss is further employed to stabilize localization training. For knowledge transfer, three specialized teachers are constructed for semantic classification, geometric regression, and structural topology supervision. A branch-decoupled adaptive weighting strategy dynamically integrates their complementary knowledge for classification and regression distillation. Experiments on DIOR show that the proposed model reduces parameters by 6.8% and GFLOPs by 13.9%, while improving mAP50 by 0.23 percentage points over YOLO11n. Validation on NWPU VHR-10 and deployment tests using PT, ONNX, and TensorRT further demonstrate improved accuracy–efficiency trade-offs and practical inference acceleration.

To address the challenges of small pest target sizes, dense distributions, strong background interference, and missed detection of small objects in insect pest monitoring lamp images, this study proposes YOLO11-MSC, a lightweight agricultural pest detection model based on an improved YOLO11. First, an Object-Centric Adaptive Slicing (OCAS) strategy is designed to construct the Insect35_OCAS dataset, which increases the relative proportion of small pest targets in the input images while reducing redundant background interference. Second, the Multi-path Nonlinear Star Aggregation (MNS) module and the C2-based Improved Bi-Level Routing Attention (C2iBRA) module are introduced to enhance fine-grained feature extraction for multi-scale pest targets and improve background suppression in complex scenes. Finally, a Semantic-Guided Adaptive Slicing (SGAS) inference strategy is developed to transfer the sliced-image training model to full-image inference on original high-resolution insect pest monitoring lamp images. Experimental results show that YOLO11-MSC achieves an mAP@0.5, Precision, and Recall of 95.2%, 91.8%, and 93.0% on the Insect35_OCAS dataset, respectively, improving the baseline YOLO11n by 1.3, 1.0, and 1.2 percentage points. The model contains only 3.4 M parameters and requires 7.8 Giga Floating-Point Operations (GFLOPs). When combined with SGAS, YOLO11-MSC achieves an mAP@0.5, Precision, and Recall of 86.4%, 81.2%, and 90.1% on the original full-image dataset, respectively. These results demonstrate that the proposed method effectively improves the detection accuracy of small pests and the full-image inference capability in complex insect pest monitoring lamp scenarios while maintaining low model complexity, providing technical support for intelligent agricultural pest monitoring and precision control.

During batch soldering, assembly of micro image sensor modules, initial random pose, and feature partially occlude target micro-component image, leading to issues of missed and erroneous detection, and low 3D spatial positioning accuracy due to cross-depth-of-field detection errors in microscopic vision. This paper proposes Small object-YOLO11-Cross-Depth-of-field Positioning (SO-YOLO11-CDP), an instance segmentation-based approach for precision cross-depth-of-field positioning micro-component. First, an improved Small object-YOLO11 (SO-YOLO11) image segmentation algorithm is designed. By incorporating a coordinate attention mechanism (CA) into segmentation head to enhance localization of micro-targets, the backbone uses non-stride convolution to preserve fine-grained feature, while target regression performance is boosted via Efficient-IoU (EIoU) loss combined with normalized Wasserstein distance (NWD). Subsequently, to further improve spatial position detection accuracy in cross-depth-of-field detection, a calibration error compensation model for image Jacobian matrix is established based on pinhole imaging principles. Experimental results indicate that SO-YOLO11 achieves 16.1% increase in precision, 4.0% increase in recall, and 9.9% increase in mean average precision (mAP0.5) over baseline YOLO11. Furthermore, it accomplishes spatial detection accuracy superior to 6.5 μm for target micro-components. The method presented in this paper holds significant engineering application value for high-precision spatial position detection of micro image sensor components.