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In high-risk industrial environments such as tunnel construction, reliable safety helmet detection is critical for preventing head injuries. However, severe illumination inhomogeneity and multi-scale object appearances pose significant challenges to existing detectors due to static anchor designs and the absence of illumination-aware feature learning. This paper proposes AE-LFOG-YOLO, an end-to-end framework that enhances YOLOv8 through dual physics-informed optimizations. The approach integrates an Illumination-Invariant Module (IIM) that employs a dual-path feature decoupling strategy to suppress lighting artifacts within the network backbone. Concurrently, the Adaptive Evolutionary - Light Field Optimized Generation (AE-LFOG) algorithm replaces static anchors with a dynamic evolutionary process guided by local illumination gradients and thin-lens imaging principles, enabling continuous optimization of anchor parameters during training. Evaluated on a real-world tunnel dataset, the method achieves 94.83% mAP@0.5 and significantly improves robustness under challenging illumination variations, as evidenced by a 35.7% extension in effective operating range. These results demonstrate the effectiveness of integrating physical imaging priors into deep learning for robust visual perception in complex industrial scenarios.

We evaluate tactile-first robotic traversal on the Department of Homeland Security (DHS) figure-8 mobility test using a two-way repeated-measures design across various algorithms (three tactile policies—M1 reactive, M2 terrain-weighted, M3 memory-augmented; a monocular camera baseline, CB-V; a tactile histogram baseline, T-VFH; and an optional tactile-informed replanner, T-D* Lite) and lighting conditions (Indoor, Outdoor, and Dark). The platform is the custom-built Eleven robot—a quadruped integrating a joint-mounted tactile tentacle with a tip force-sensitive resistor (FSR; Walfront 9snmyvxw25, China; 0–10 kg range, ≈0.1 N resolution @ 83 Hz) and a woven Galvorn carbon-nanotube (CNT) yarn for proprioceptive bend sensing. Control and sensing are fully wireless via an ESP32-S3, Arduino Nano 33 BLE, Raspberry Pi 400, and a mini VESC controller. Across 660 trials, the tactile stack maintained ∼21 ms (p50) policy latency and mid-80% success across all lighting conditions, including total darkness. The memory-augmented tactile policy (M3) exhibited consistent robustness relative to the camera baseline (CB-V), trailing by only ≈3–4% in Indoor and ≈13–16% in Outdoor and Dark conditions. Pre-specified, two one-sided tests (TOSTs) confirmed no speed equivalence in any M3↔CB-V comparison. Unlike vision-based approaches, tactile-first traversal is invariant to illumination and texture—an essential capability for navigation in darkness, smoke, or texture-poor, confined environments. Overall, these results show that a tactile-first, memory-augmented control stack achieves lighting-independent traversal on DHS benchmarks while maintaining competitive latency and success, trading modest speed for robustness and sensing independence.

Accurate indoor localization for unmanned aerial vehicles (UAVs) remains challenging in GPS-denied environments, especially for small-object detection and under low-light conditions. We propose Robust Wavelet-Aware YOLO (RWA-YOLO), a vision-based detection framework that integrates a wavelet-aware attention fusion module with a dual multi-path aggregation mechanism to enhance small-object detection and multi-scale feature representation. UAV-mounted LEDs are utilized to ensure robust visual perception in low-light indoor scenarios. The UAV’s three-dimensional position is estimated through multi-view geometric triangulation without relying on external beacons or artificial markers. Beyond static localization, the system is validated under dynamic flight conditions, demonstrating smooth and temporally coherent trajectory reconstruction suitable for real-time control loops (update rate ≈25FPS). Extensive experiments in real indoor environments achieve centimeter-level localization accuracy (root mean square error: 9.9 mm, 95th percentile error: 13.5 mm), outperforming state-of-the-art vision-based methods and achieving accuracy comparable to or better than representative hybrid ultra-wideband–vision systems reported in the literature. These results confirm the effectiveness, robustness, and real-time capability of RWA-YOLO for indoor UAV navigation in constrained environments.

Segmentation of tomato fruits under complex lighting conditions remains technically challenging, especially in low illumination or overexposure, where RGB-only methods often suffer from blurred boundaries and missed small or occluded instances, and simple multimodal fusion cannot fully exploit complementary cues. To address these gaps, we propose YOLO-MSRF, a lightweight RGB–NIR multimodal segmentation and refinement framework for robust tomato perception in facility agriculture. Firstly, we propose a dual-branch multimodal backbone, introduce Cross-Modality Difference Complement Fusion (C-MDCF) for difference-based complementary RGB–NIR fusion, and design C2f-DCB to reduce computation while strengthening feature extraction. Furthermore, we develop a cross-scale attention fusion network and introduce the proposed MS-CPAM to jointly model multi-scale channel and position cues, strengthening fine-grained detail representation and spatial context aggregation for small and occluded tomatoes. Finally, we design the Multi-Scale Fusion and Semantic Refinement Network, MSF-SRNet, which combines the Scale-Concatenate Fusion Module (Scale-Concat) fusion with SDI-based cross-layer detail injection to progressively align and refine multi-scale features, improving representation quality and segmentation accuracy. Extensive experiments show that YOLO-MSRF achieves substantial gains under weak and low-light conditions, where RGB-only models are most prone to boundary degradation and missed instances, and it still delivers consistent improvements on the mixed four-light validation set, increasing mAP0.5 by 2.3 points, mAP0.5–0.95 by 2.4 points, and mIoU by 3.60 points while maintaining real-time inference at 105.07 FPS. The proposed system further supports counting, size estimation, and maturity analysis of harvestable tomatoes, and can be integrated with depth sensing and yield estimation to enable real-time yield prediction in practical greenhouse operations.

Accurate target perception in unstructured outdoor environments remains a fundamental challenge in computational imaging and machine vision, primarily due to severe optical degradation caused by variable illumination, specular highlights, and dense foliage occlusion. Existing optical sensing systems often struggle to maintain robustness under these physical constraints, especially when deployed on edge devices with strict computational limits. To address these challenges, this paper proposes Orchard-YOLO, a lightweight, computationally efficient object detection network designed to maintain robustness against environmental and optical noise in complex orchard environments. Unlike generic architectures, Orchard-YOLO introduces three architectural enhancements for robust detection: (1) a High-Resolution P2 Detection Head to preserve high-frequency optical details and fine-grained texture cues often lost during digital downsampling; (2) Coordinate Attention (CA) mechanisms integrated into the feature fusion pathway to filter out background optical interference and enhance spatial discrimination for heavily occluded targets; and (3) a Ghost-convolution-based backbone to optimize the inference pipeline for real-time edge processing. Evaluated on a comprehensive multi-fruit dataset under simulated optical stress (including ±50% illumination variation and up to 70% occlusion), Orchard-YOLO achieves 94.8% mAP@0.5. It shows improved robustness under illumination variation and occlusion compared to baseline models, while achieving up to 25 FPS on an NVIDIA Jetson Nano edge device. These results suggest that Orchard-YOLO offers a detection framework suitable for resource-constrained orchard perception.

Feature detection is the basis of many computer vision applications. However, the existing feature detectors have poor illumination robustness for various reasons. FAST is a very effective detection method, and is currently widely used for real-time feature detection. The threshold function in the traditional FAST method is a linear function and is unable to deal with the issue of illumination robustness. This paper proposes an illumination-robust feature detection method, the core is an adaptive threshold FAST. The proposed method constructs a threshold function based on neighborhood standard deviation, which successfully solves the problem that the traditional FAST has poor illumination robustness. In addition, a new image preprocessing method consisting of homomorphic filtering and histogram equalization is introduced to the front-end of the proposed method in order to improve the quality of the input image. Compared with state-of-the-art methods, the repeatibility rate of proposed method has been increased several times in the underexposure matching experiment, and the number of repeated features has been increased by dozens of times. Meanwhile, the number of repeated features increased by more than a third on average in the overexposed experiment. The experimental results strongly prove that the proposed method has significant advantages in terms of repeatibility rate, number of repeated features and detection stability evaluation indices.

Face recognition under uncontrolled lighting remains challenging due to variations in brightness, background noise, and low-quality features. This paper presents a unified deep learning model that integrates illumination normalization, skin-aware spatial modulation, and quality-based margin learning within a single inference process. Unlike earlier methods that treat relighting or segmentation as preprocessing, this approach directly integrates mask-guided feature modulation into embedding learning. The system comprises RetinaFace detection, photometric augmentation during training, lightweight neural relighting at inference, U-Net-based skin segmentation, and identity embeddings trained with ArcFace, AdaFace, or MagFace losses, with angular margins adapted to feature quality. Experiments on Labeled Faces in the Wild (LFW), Celebrities in Frontal-Profile (CFP-FP), Age Database 30 (AgeDB-30), and a custom illumination dataset demonstrate steady enhancements in difficult lighting conditions. The model reaches a competitive 99.8% accuracy on LFW and shows notable improvements on pose-hard CFP-FP and the custom dataset, such as a +2.6% increase in TPR at 1 × 10−4 FPR. The key innovations include: (i) mask-guided embedding modulation that embeds segmentation into feature learning, (ii) a dual strategy combining training-time photometric data augmentation with inference-time neural relighting, and (iii) joint spatial–quality margin learning via AdaFace/MagFace. Finally, results confirm consistent gains under challenging illumination and pose variations.

Accurate modeling of non-cooperative space targets remains a significant challenge, particularly under complex illumination conditions. A hybrid virtual–real framework is proposed that integrates photometric compensation, 3D reconstruction, and visibility determination to enhance the robustness and accuracy of monocular-based modeling systems. To overcome the breakdown of the classical photometric constancy assumption under varying illumination, a compensation-based photometric model is formulated and implemented. A point cloud–driven virtual space is constructed and refined through Poisson surface reconstruction, enabling per-pixel depth, normal, and visibility information to be efficiently extracted via GPU-accelerated rendering. An illumination-aware visibility model further distinguishes self-occluded and shadowed regions, allowing for selective pixel usage during photometric optimization, while motion parameter estimation is stabilized by analyzing angular velocity precession. Experiments conducted on both Unity3D-based simulations and a semi-physical platform with robotic hardware and a sunlight simulator demonstrate that the proposed method consistently outperforms conventional feature-based and direct SLAM approaches in trajectory accuracy and 3D reconstruction quality. These results highlight the effectiveness and practical significance of incorporating virtual space feedback for non-cooperative space target modeling.

The common feature matching algorithms for street view images are sensitive to the illumination changes in augmented reality (AR), this may cause low accuracy of matching between street view images. This paper proposes a novel illumination insensitive feature descriptor by integrating the center-symmetric local binary pattern (CS-LBP) into a common feature description framework. This proposed descriptor can be used to improve the performance of eight commonly used feature-matching algorithms, e.g., SIFT, SURF, DAISY, BRISK, ORB, FREAK, KAZE, and AKAZE. We perform the experiments on five street view image sequences with different illumination changes. By comparing with the performance of eight original algorithms, the evaluation results show that our improved algorithms can improve the matching accuracy of street view images with changing illumination. Further, the time consumption only increases a little. Therefore, our combined descriptors are much more robust against light changes to satisfy the high precision requirement of augmented reality (AR) system.

Under uneven illumination, the performances degrade significantly for some existing face recognition methods. It is a challenge for face recognition methods to work effectively under different illumination conditions. In this paper, an illumination robust face recognition method, based on random projection and sparse representation, is proposed. In the proposed method, face images are preliminary illumination normalized by gamma correction and difference of Gaussian filtering, and then several projection spaces are obtained by iterative random projection, followed by constructing an initial sample space using Fisher discrimination analysis. This scheme enriches the discrimination abilities of sample features and achieves the security and completeness for biometric template. Test samples are sparsely decomposed into each subspace, and based on statistical average residual, a modified sparse representation method is proposed to realize face recognition with higher stability and illumination robustness. Experimental results indicate that the proposed method provides competitive performance with acceptable computational efficiency. Specifically, for the five subsets of Yale B database, our approach achieves 99.74% average recognition rate, which performs higher accuracy than that of comparative methods.