The semantic segmentation (SS) of low-contrast images (LCIs) remains a significant challenge in computer vision, particularly for sensor-driven applications like medical imaging, autonomous navigation, and industrial defect detection, where accurate object delineation is critical. This systematic review develops a comprehensive evaluation of state-of-the-art deep learning (DL) techniques to improve segmentation accuracy in LCI scenarios by addressing key challenges such as diffuse boundaries and regions with similar pixel intensities. It tackles primary challenges, such as diffuse boundaries and regions with similar pixel intensities, which limit conventional methods. Key advancements include attention mechanisms, multi-scale feature extraction, and hybrid architectures combining Convolutional Neural Networks (CNNs) with Vision Transformers (ViTs), which expand the Effective Receptive Field (ERF), improve feature representation, and optimize information flow. We compare the performance of 25 models, evaluating accuracy (e.g., mean Intersection over Union (mIoU), Dice Similarity Coefficient (DSC)), computational efficiency, and robustness across benchmark datasets relevant to automation and robotics. This review identifies limitations, including the scarcity of diverse, annotated LCI datasets and the high computational demands of transformer-based models. Future opportunities emphasize lightweight architectures, advanced data augmentation, integration with multimodal sensor data (e.g., LiDAR, thermal imaging), and ethically transparent AI to build trust in automation systems. This work contributes a practical guide for enhancing LCI segmentation, improving mean accuracy metrics like mIoU by up to 15% in sensor-based applications, as evidenced by benchmark comparisons. It serves as a concise, comprehensive guide for researchers and practitioners advancing DL-based LCI segmentation in real-world sensor applications.

The development of autonomous vehicles has grown significantly recently due to the promise of improving safety and productivity in cities and industries. The scene perception module has benefited from the latest advances in computer vision and deep learning techniques, allowing the creation of more accurate and efficient models. This study develops and evaluates semantic segmentation models based on a bilateral architecture to enhance the detection of traversable areas for autonomous vehicles on unstructured routes, particularly in datasets where the distinction between the traversable area and the surrounding ground is minimal. The proposed hybrid models combine Convolutional Neural Networks (CNNs), Vision Transformer (ViT), and Multilayer Perceptron (MLP) techniques, achieving a balance between precision and computational efficiency. The results demonstrate that these models outperform the base architectures in prediction accuracy, capturing distant details more effectively while maintaining real-time operational capabilities.

Intelligent autonomous systems in open-pit mining operations face critical challenges in perception and decision-making due to sensor-based visual degradations, particularly lens soiling and sun glare, which significantly compromise the performance and safety of integrated mining automation systems. We propose a comprehensive intelligent framework leveraging single-domain generalization with traditional data augmentation techniques, specifically Photometric Distortion (PD) and Contrast Limited Adaptive Histogram Equalization (CLAHE), integrated within the BiSeNetV1 architecture. Our systematic approach evaluated four state-of-the-art backbones: ResNet-50, MobileNetV2 (Convolutional Neural Networks (CNN)-based), SegFormer-B0, and Twins-PCPVT-S (ViT-based) within an end-to-end autonomous system architecture. The model was trained on clean images from the AutoMine dataset and tested on degraded visual conditions without requiring architectural modifications or additional training data from target domains. ResNet-50 demonstrated superior system robustness with mean Intersection over Union (IoU) of 84.58% for lens soiling and 80.11% for sun glare scenarios, while MobileNetV2 achieved optimal computational efficiency for real-time autonomous systems with 55.0 Frames Per Second (FPS) inference speed while maintaining competitive accuracy (81.54% and 71.65% mIoU respectively). Vision Transformers showed superior stability in system performance but lower overall performance under severe degradations. The proposed intelligent augmentation-based approach maintains high accuracy while preserving real-time computational efficiency, making it suitable for deployment in autonomous mining vehicle systems. Traditional augmentation approaches achieved approximately 30% superior performance compared to advanced GAN-based domain generalization methods, providing a practical solution for robust perception systems without requiring expensive multi-domain training datasets.

Cameras and IMUs on heavy mining trucks supply the visual signal that Advanced Driver Assistance Systems (ADASs) use in open-pit operations. Haul roads in a surface mine are unstructured and unmarked, so a perception model must be both accurate and fast. We address this with a video-based multitask pipeline for a mining Driver Support System (DSS): a single BiSeNetV1 network produces drivable-area segmentation and steering-direction classification in one forward pass. Training used only 100 frames sampled non-sequentially from in-cab recordings of a real open-pit mine; evaluation used two full onboard sequences. To exploit temporal redundancy without annotating video, we propose an Adaptive Clockwork (A-CW) inference scheme: the spatial path runs on every frame, while the context path is refreshed only on keyframes whose cadence is set by the classification output, the same signal shown to the driver as a steering hint. This classification-guided policy increases context updates on curved segments, where the scene changes more rapidly, and reduces them on straight sections, where semantic redundancy is higher. The selected A-CW configuration was evaluated on full temporal test sequences, including one route kept entirely outside the training source. On this unseen route, A-CW achieved 94.70% road-class IoU and 73.68% Top-1 Accuracy. GPU-only throughput increased from about 55 FPS with frame-by-frame inference to 168.01 FPS, and display-excluded end-to-end processing in the simulated ADAS pipeline remained at approximately 37.5 FPS.