Reliable road condition detection using drone imagery is critically important, particularly under harsh weather conditions such as rain, fog, and snow, which cause reduced visibility and blurred objects. Traditional detection methods are limited in effectively handling these severe scenarios due to their static feature extraction approaches. To address these challenges, we propose an innovative affine lie group convolution and weather-adaptive feature enhancement network (ALGC-WFEMNet). The core innovation of this method lies in the affine lie group convolution (ALGC), which leverages the mathematical framework of affine lie groups to introduce a dynamic convolution mechanism. This mechanism adaptively modifies convolution kernels based on affine transformations, significantly enhancing the model’s robustness against weather-induced variations in scale, rotation, and visibility. Furthermore, the ALGC framework integrates a learning-based weather condition coefficient, dynamically adjusting kernel responses to specific environmental conditions such as rain, fog, and snow. This theoretical advancement not only emphasizes the mathematical novelty of applying affine lie groups in convolutional neural networks but also substantially improves feature extraction and adaptability for object detection tasks. Experimental validation on UAV-based road inspection datasets demonstrates that our ALGC-WFEMNet achieves a mean average precision (mAP) of 60.48%. Furthermore, we deploy the model within a UAV-IoT system to verify its practical effectiveness, achieving an inference time of 23.31 seconds on a Raspberry Pi.

Detection of rotating targets in complex remote sensing scenarios often suffers from angular inconsistencies and boundary jitter, especially for small-to-medium objects with rapid pose changes or indistinct boundaries in dense environments. To address this, we propose ASBPNet, a unified framework coupling geometric alignment with policy adaptation. It features the following: (1) Angle-Synchronized Graph (ASG), which injects angle–alignment relationships and residual-based boundary refinement to improve rotational consistency and reduce boundary errors for small objects; (2) Bilevel Policy Optimization (BPO), which unifies control over rotation enhancement, sample allocation, block scanning, and rotational NMS for cross-stage policy coordination and improved recall. Together, ASG and BPO form a tightly coupled pipeline in which geometric alignment directly reinforces policy optimization, yielding mutually enhanced rotation robustness, boundary stability, and detection recall across densely distributed remote sensing scenes. We conducted systematic evaluations on datasets including DIOR-R, HRSC2016, and DOTAv1.0: compared to baselines, overall accuracy achieved significant improvement on DIOR-R, with performance reaching 98.2% on HRSC2016. Simultaneously, enhanced robustness and boundary stability were demonstrated in complex backgrounds and dense small-object scenarios, validating the synergistic value of geometric alignment and policy adaptation.

Potato leaf diseases directly reduce yield and quality, and accurate field detection is important for precision plant protection. However, potato disease lesions are often weak in deep semantic representation, easily disturbed by complex field backgrounds, and variable in multi-scale lesion texture. To address these challenges, this study proposes an improved YOLO-based potato leaf disease detection model. The proposed model enhances the detector through three task-oriented modules. Deep Symptom Enhancement is used to strengthen deep disease feature extraction. Lesion Selection Attention based on large separable kernel attention improves the spatial selection of lesion regions. Multi-Scale Refinement Adapter uses a Mona-based C2PSA structure with two stacked Mona adapters to refine multi-scale texture and lesion-boundary information. Experiments were conducted on a potato leaf disease image dataset using mAP50, average recall (AR), parameters, GFLOPs, and FPS as evaluation metrics. The baseline YOLO26s achieved 81.31% mAP50 and 77.85% AR. The proposed SLR-YOLO model achieved 88.92% mAP50 and 83.51% AR, improving mAP50 and AR by 7.61 and 5.66 percentage points, respectively, while maintaining 118.6 FPS. The results show that the proposed framework improves detection accuracy for potato leaf disease images while retaining practical real-time performance.

Reliable road pothole detection remains challenging in complex environments, where low contrast, shadows, water films, and strong background textures cause frequent false alarms, missed detections, and boundary instability. Thin rims and adjacent objects further complicate localization, and model robustness often deteriorates across regions and sensor domains. To address these issues, we propose OCDBMamba, a unified and efficient framework that integrates omnidirectional context modeling with consensus-driven boundary selection. Specifically, we introduce the following: (1) an Omnidirectional Channel-Selective Scanning (OCS) mechanism that aggregates long-range structural cues by performing multidirectional scans and channel similarity fusion with cross-directional consistency, capturing comprehensive spatial dependencies at near-linear complexity and (2) a Dual-Branch Consensus Thresholding (DBCT) module that enforces branch-level agreement with sparsity-regulated adaptive thresholds and boundary consistency constraints, effectively preserving true rims while suppressing reflections and redundant responses. Extensive experiments on normal, shadowed, wet, low-contrast, and texture-rich subsets yield 90.7% $mA{P}_{50}$, 67.8% $mA{P}_{50:95}$, a precision of 0.905, and a recall of 0.812 with 13.1 GFLOPs, outperforming YOLOv11n by 5.4% and 5.6%, respectively. The results demonstrate more stable localization and enhanced robustness under diverse conditions, validating the synergy of OCS and DBCT for practical road inspection and on-vehicle perception scenarios.

The deployment of lightweight object detectors on remote sensing edge platforms is severely constrained by the rigid trade-off between perception capacity and metabolic expenditure. To solve this fundamental challenge, we draw inspiration from the superior energy efficiency of the mammalian brain and the principles of connectomics to introduce CONERSLite. By emulating the dual mode synergy of biological neural systems, CONERSLite integrates a Compact Anatomical Backbone (CAB) representing the stable anatomical connectome and a Functional Connectome Router (FCR) that mimics the plasticity of the functional connectome. Our framework achieves a peak mAP of 82.35% on the DOTA-v1.0 dataset with only 28.3 M parameters and 195 G FLOPs, effectively establishing a new accuracy–efficiency Pareto frontier for remote sensing. On the HRSC2016 dataset, it reaches a state-of-the-art mAP of 98.62% while reducing the total parameter count by approximately 45% compared to high-precision optimized models like RTMDet. These results demonstrate that the application of connectomics principles provides a biologically grounded and highly efficient solution for resource-constrained remote sensing object detection.