Semantic segmentation in street scenes is a crucial technology for autonomous driving to analyze the surrounding environment. In street scenes, issues such as high image resolution caused by a large viewpoints and differences in object scales lead to a decline in real-time performance and difficulties in multi-scale feature extraction. To address this, we propose a bilateral-branch real-time semantic segmentation method based on semantic information distillation (BSDNet) for street scene images. The BSDNet consists of a Feature Conversion Convolutional Block (FCB), a Semantic Information Distillation Module (SIDM), and a Deep Aggregation Atrous Convolution Pyramid Pooling (DASP). FCB reduces the semantic gap between the backbone and the semantic branch. SIDM extracts high-quality semantic information from the Transformer branch to reduce computational costs. DASP aggregates information lost in atrous convolutions, effectively capturing multi-scale objects. Extensive experiments conducted on Cityscapes, CamVid, and ADE20K, achieving an accuracy of 81.7 $\mathrm{\%}$ Mean Intersection over Union (mIoU) at 70.6 Frames Per Second (FPS) on Cityscapes, demonstrate that our method achieves a better balance between accuracy and inference speed.

Rail fasteners are a crucial component of the railway transportation safety system. These fasteners, distinguished by their high length-to-width ratio, frequently encounter elevated failure rates, necessitating manual inspection and maintenance. Manual inspection not only consumes time but also poses the risk of potential oversights. With the advancement of deep learning technology in rail fasteners, challenges such as the complex background of rail fasteners and the similarity in their states are addressed. We have proposed an efficient and high-precision rail fastener detection algorithm, named YOLO-O2E (you only look once-O2E). Firstly, we propose the EFOV (Enhanced Field of View) structure, aiming to adjust the effective receptive field size of convolutional kernels to enhance insensitivity to small spatial variations. Additionally, The OD_MP (ODConv and MP_2) and EMA (Efficient Multi-Scale Attention) modules mentioned in the algorithm can acquire a wider spectrum of contextual information, enhancing the model’s ability to recognize and locate objectives. Additionally, we collected and prepared the GKA dataset, sourced from real train tracks. Through testing on the GKA dataset and the publicly available NUE-DET dataset, our method outperforms general-purpose object detection algorithms. On the GKA dataset, our model achieved a mAP@0.5 value of 97.6% and a mAP@0.5:0.95 value of 83.9%, demonstrating excellent inference speed. YOLO-O2E is an algorithm for detecting anomalies in railway fasteners that is applicable in practical industrial settings, addressing the industry gap in rail fastener detection.

While single-modal visible light images or infrared images provide limited information, infrared light captures significant thermal radiation data, whereas visible light excels in presenting detailed texture information. Combining images obtained from both modalities allows for leveraging their respective strengths and mitigating individual limitations, resulting in high-quality images with enhanced contrast and rich texture details. Such capabilities hold promising applications in advanced visual tasks including target detection, instance segmentation, military surveillance, pedestrian detection, among others. This paper introduces a novel approach, a dual-branch decomposition fusion network based on AutoEncoder (AE), which decomposes multi-modal features into intensity and texture information for enhanced fusion. Local contrast enhancement module (CEM) and texture detail enhancement module (DEM) are devised to process the decomposed images, followed by image fusion through the decoder. The proposed loss function ensures effective retention of key information from the source images of both modalities. Extensive comparisons and generalization experiments demonstrate the superior performance of our network in preserving pixel intensity distribution and retaining texture details. From the qualitative results, we can see the advantages of fusion details and local contrast. In the quantitative experiments, entropy (EN), mutual information (MI), structural similarity (SSIM) and other results have improved and exceeded the SOTA (State of the Art) model as a whole.

Semantic segmentation of driving scene images is crucial for autonomous driving. While deep learning technology has significantly improved daytime image semantic segmentation, nighttime images pose challenges due to factors like poor lighting and overexposure, making it difficult to recognize small objects. To address this, we propose an Image Adaptive Enhancement (IAEN) module comprising a parameter predictor (Edip), multiple image processing filters (Mdif), and a Detail Processing Module (DPM). Edip combines image processing filters to predict parameters like exposure and hue, optimizing image quality. We adopt a novel image encoder to enhance parameter prediction accuracy by enabling Edip to handle features at different scales. DPM strengthens overlooked image details, extending the IAEN module’s functionality. After the segmentation network, we integrate a Depth Guided Filter (DGF) to refine segmentation outputs. The entire network is trained end-to-end, with segmentation results guiding parameter prediction optimization, promoting self-learning and network improvement. This lightweight and efficient network architecture is particularly suitable for addressing challenges in nighttime image segmentation. Extensive experiments validate significant performance improvements of our approach on the ACDC-night and Nightcity datasets.