Underwater object detection technology is extensively applied in subsea resource exploration and benthic environmental monitoring; however, the underwater environment presents significant challenges due to limited visibility caused by insufficient illumination and turbid water quality, severely impeding detection tasks. To address these challenges and enhance precision, this paper proposes FEFM-YOLO11—an enhanced YOLO11-based underwater object detection algorithm. Primarily, the algorithm substitutes two C3K2 modules in the backbone with a Feature Enhancement Module (FEM). Employing a multi-branch dilated convolutional structure, the FEM increases feature diversity, expands the network’s local receptive field, and enhances semantic representation of small objects, thereby strengthening detection capabilities. Subsequently, a Context-Aware Fusion Module (CAFM) is introduced in the Neck, which effectively models global and local features through integrated local feature capture (convolutional operations) and global feature extraction (attention mechanisms), consequently improving denoising performance. Furthermore, a dedicated small-target detection layer is incorporated to specifically boost detection performance for smaller underwater objects. Finally, the Wise IoU loss function was used for comprehensive evaluation and performance optimization, and the collaborative integration of these components effectively reduced the problem of missed detections caused by occlusion in dense cluster targets. Experimental results on the URPC2020 dataset demonstrate that the improved algorithm achieves enhancements of 2.0% points in mAP@50 and 1.9% points in mAP@50:95 compared to the baseline, confirming that FEFM-YOLO11 elevates detection precision and validates the feasibility and effectiveness of the proposed methodology.

Plant disease detection in natural environments is significantly challenged by variations in lesion scales and interference from complicated background clutter. Nevertheless, current models often remain limited in effectively capturing multi-scale features and mitigating background interference simultaneously. To tackle these challenges, we present TGL-YOLO, an improved detection network built on the YOLO11 framework. Methodologically, we introduce the Tri-Scale Dynamic Block (TSDBlock) to adaptively extract fine-grained features across highly variable lesion sizes. Furthermore, a Gated Pyramid Spatial Transformer (GPST) is designed to fuse cross-scale features and suppress background interference, while a Large Separable Pyramid Attention (LSPA) module expands the spatial receptive field to capture global context. Experimental results on two public datasets show that TGL-YOLO demonstrates improved performance over the YOLO11s baseline. On the PlantDoc dataset, it improves mAP50 and mAP50:95 by 4.7% and 3.7%, reaching 0.591 and 0.449, respectively. On the FieldPlant dataset, it reaches 0.793 and 0.608, yielding improvements of 2.3% and 1.9%. The proposed method demonstrates the capability to reduce missed detections and false positives caused by multi-scale lesions and environmental noise, providing a competitive and computationally viable solution for agricultural disease monitoring in natural environments.

To improve the positioning accuracy of farming vehicle in precision agriculture, an integrated positioning system is proposed based on Global Navigation Satellite System (GNSS)/Strapdown Inertial Navigation System (SINS)/Wireless Sensor Networks (WSN) with low cost and high reliability. The principles of commonly used localization technologies in vehicle positioning are compared and the Received Signal Strength Indication (RSSI) based measurement method is chosen as the integrated positioning system for information fusion considering the complexity of the algorithm, positioning accuracy and hardware requirements in the application scenario. The research of wireless signal propagation loss model of farmland environment was conducted. A set of GNSS/SINS/WSN integrated positioning system is designed and implemented based on the requirement analysis of low cost, high reliability and precision. Simulation experiments were conducted and the results show that integrated positioning system can improve the position accuracy of agricultural machinery and meet the precision positioning requirements of agricultural machinery.

Object detection in UAV imagery is hindered by micro-scale targets, dense distributions, and cluttered backgrounds, where existing detectors fail to simultaneously achieve high accuracy and real-time throughput. We propose ECP-YOLO, a lightweight framework built on YOLOv12s, incorporating four modules: Pinwheel Convolution (PConv) for direction-selective geometric modeling, a Context Refiner Block (CRB) for spatially gated background suppression, an Edge-Aware Attention Fusion Module (EAFM) for structural boundary preservation, and a Progressive Inter-Scale Feature Fusion (PISF) strategy for cascaded cross-scale detail propagation, alongside a high-resolution P2 detection head. On VisDrone2019, ECP-YOLO achieves 38.1% mAP@0.5 and 22.1% mAP@0.5:0.95, surpassing YOLOv12s by 6.3% and 3.5% at 79 FPS. On UAVDT, Precision improves from 27.0% to 34.1% and mAP@0.5 from 28.7% to 30.4%, demonstrating cross-dataset transferability. These results demonstrate that ECP-YOLO achieves competitive accuracy–efficiency trade-offs for real-time UAV detection in complex environments.

With the rapid evolution of remote sensing earth observation technology, high-resolution object detection is crucial in military and civilian domains but faces challenges from expansive views and complex backgrounds. Small objects are particularly challenging due to their low pixel coverage, poor textures, and susceptibility to drastic illumination changes and background clutter. To address these problems, this paper proposes MDCA-YOLO for oriented object detection. A Dual-Branch Perception Module (DBPM) is designed utilizing a synergistic mechanism of large-kernel and strip convolutions to establish long-range dependencies, accurately capturing geometric features of tiny objects even in the absence of local details; Multi-Adaptive Selection Fusion (MASF) is proposed to address cross-scale feature loss by adaptively enhancing feature response while suppressing background noise; furthermore, a reconstructed decoupled detection head, CoordAttOBB, significantly improves angle regression accuracy while reducing complexity. Experimental results on the DIOR-R dataset show MDCA-YOLO surpasses YOLO11s, improving mAP50 and mAP50:95 by 2.5% and 2.7%, respectively, effectively proving the algorithm’s superiority in remote sensing tasks.

A novel adaptive CMOS low-dropout regulator (LDO) is proposed for DDR4 memory power supply. By using a two stage structure, this LDO can adjust its transfer path to afford a very large range of load current from 0 A to 3 A. Two NMOS pass elements are used to do both sourcing and sinking functions, which are not included in traditional LDOs. The improved structure with two NMOS transistors performs a good stability by using Miller compensation method. Fast load transition is also obtained due to 20 μF ceramic capacitor at output terminal. The circuit has been demonstrated in TSMC 0.18μm standard CMOS process with a 1.2 V input voltage under 3.3 V supply voltage. The simulation results show that the LDO consumes a quiescent current of 70 μA, the phase margin is at least 47 degree with 1 mA to 3 A load current. The transient output voltage is constrained within 40 mV when load current ramps from 0 A to 3 A with 20 μF capacitor load.