Multi-Domain Image Semantic Segmentation Algorithm Based on Improved UNET

In the field of remote sensing, using a large amount of labeled image data to supervise the training of fully convolutional networks for the semantic segmentation of images is expensive. However, using a small amount of labeled data can lead to reduced network performance. This paper proposes an unsupervised semantic segmentation method that combines the ImSE-Net model with SLICm superpixel optimization. First, the ImSE-Net model is used to extract semantic features from the image to obtain rough semantic segmentation results. Then, the SLICm superpixel segmentation algorithm is used to segment the input image into superpixel images. Finally, an unsupervised semantic segmentation model (UGLS) is used to combine high-level abstract semantic features with detailed information on superpixels to obtain edge-optimized semantic segmentation results. Experimental results show that compared with other semantic segmentation algorithms, our method more effectively handles unbalanced areas, such as object boundaries, and achieves better segmentation results, with higher semantic consistency.

Aiming at the problem that the semantic segmentation algorithm has poor segmentation results in large depth of field (DOF) images, this paper proposed the concept of depth IoU (dIoU) evaluation standard. This concept based on the effective generalized IoU-loss (GIoU-loss) and the instance-level IoU (iIoU) concept proposed by Cityscapes dataset in the field of target detection. This paper introduced the image depth information into the loss function in the semantic segmentation algorithm. By using dIoU as a evaluation standard, the detection effect of the distant object in the DOF picture will get more weight. It solves the problem of weight reduction caused by the far-reaching effect of the distant object in the traditional evaluation standard.

This paper aims to address the issue of traditional art patterns not fully leveraging contextual information in image semantic segmentation. It proposes a multi-level feature aggregation method for image semantic segmentation based on visual Transformer and coordinate attention adjustment (CA Adjustment). Initially, the input image is segmented into slices for linear projection, incorporating learnable positional embeddings to derive an encoded input sequence. Subsequently, employing a visual Transformer-based encoder, the image is transformed into patches to capture global context across the network. CA Adjustment is then introduced to adaptively merge fusion features and visual backbone features using weighted adjustments. Lastly, a multi-objective grasshopper optimization algorithm is designed based on a clustering evolution mechanism to further enhance algorithm performance. Extensive experimental results demonstrate that our proposed algorithm effectively models global contextual information for image feature extraction. Compared to existing advanced algorithms, our method achieves high segmentation accuracy in semantic segmentation tasks, consistently exceeding 95%. Ablation experiments further validate the efficacy of our approach, underscoring its broad applicability in high-precision image semantic segmentation.

An image semantic segmentation algorithm using fully convolutional network (FCN) integrated with the recently proposed simple linear iterative clustering (SLIC) that is based on boundary term (BSLIC) is developed. To improve the segmentation accuracy, the developed algorithm combines the FCN semantic segmentation results with the superpixel information acquired by BSLIC. During the combination process, the superpixel semantic annotation is newly introduced and realized by the four criteria. The four criteria are used to annotate a superpixel region, according to FCN semantic segmentation result. The developed algorithm can not only accurately identify the semantic information of the target in the image, but also achieve a high accuracy in the positioning of small edges. The effectiveness of our algorithm is evaluated on the dataset PASCAL VOC 2012. Experimental results show that the developed algorithm improved the target segmentation accuracy in comparison with the traditional FCN model. With the BSLIC superpixel information that is involved, the proposed algorithm can get 3.86%, 1.41%, and 1.28% improvement in pixel accuracy (PA) over FCN-32s, FCN-16s, and FCN-8s, respectively.

<p indent=0mm>An important part of autonomous driving is the perception of the driving environment of the car, which has created a strong demand for high precision semantic segmentation algorithms that can be run in real time on low-power mobile devices. However, when analyzing the factors that affect the accuracy and speed of the semantic segmentation network, it can be found that in the structure of the previous semantic segmentation algorithm, spatial information and context features are difficult to take into account at the same time, and using two networks to obtain spatial information and context information separately will increase the amount of calculation and storage. Therefore, a new structure is proposed that divides the spatial path and context path from the network based on the residual structure, and a two-path real-time semantic segmentation network is designed based on this structure. The network contains a feature fusion module and an attention refinement module, which are used to realize the function of fusing the multi-scale features of two paths and optimizing the output results of context path. The network is based on the PyTorch framework and uses NVIDIA 1080Ti graphics cards for experiments. On the road scene data set Cityscapes, mIoU reached 78.8%, and the running speed reached 27.5 fps.

The groundbreaking segment anything model (SAM), built on a vision transformer (ViT) design with millions of parameters and trained on the large SA-1B dataset, acts as a vision foundation model that can be used for various segmentation tasks. However, this model cannot be directly applied to the semantic segmentation of remote sensing images, as it tends to over-segment objects rather than preserving their semantics. Furthermore, it does not align well with object boundaries, which are usually required for the high-accuracy segmentation of high-resolution remote sensing images. To address this issue, we adjust the image encoder and mask decoder of SAM and propose an HL-SAM-Seg network. The image encoder is extensively adjusted to comprise an adapter, a high-resolution (high-res) path, and a low-resolution (low-res) path. The latter two paths extract and update the high-res and low-res features, which are merged in the mask decoder to perform multi-class segmentation. Specifically, we design a highlow- resolution cross-attention (HL) module inserted into the transformer blocks of the low-res path to align and update the low-res and high-res features. Experimental results on the ISPRS Vaihingen, ISPRS Potsdam, and FloodNet datasets show that the proposed HL-SAM-Seg outperformed conventional state-of-theart semantic segmentation algorithms overall, with underperformance in some small sample categories. Moreover, it has fewer trainable parameters, suggesting the potential for leveraging SAM for remote sensing image segmentation.

Unpaired image-to-image translation is the problem of mapping an image in the source domain to one in the target domain, without requiring corresponding image pairs. To ensure the translated images are realistically plausible, recent works, such as Cycle-GAN, demands this mapping to be invertible. While, this requirement demonstrates promising results when the domains are unimodal, its performance is unpredictable in a multi-modal scenario such as in an image segmentation task. This is because, invertibility does not necessarily enforce semantic correctness. To this end, we present a semantically-consistent GAN framework, dubbed Sem-GAN, in which the semantics are defined by the class identities of image segments in the source domain as produced by a semantic segmentation algorithm. Our proposed framework includes consistency constraints on the translation task that, together with the GAN loss and the cycle-constraints, enforces that the images when translated will inherit the appearances of the target domain, while (approximately) maintaining their identities from the source domain. We present experiments on several image-to-image translation tasks and demonstrate that Sem-GAN improves the quality of the translated images significantly, sometimes by more than 20% on the FCN score. Further, we show that semantic segmentation models, trained with synthetic images translated via Sem-GAN, leads to significantly better segmentation results than other variants.

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Railway intrusion seriously threatens railway safety and may cause serious casualties and huge property losses. With the development of autonomous driving, image processing algorithms based on vehicle front video have developed rapidly. This paper proposes a railway intrusion detection framework based on vehicle front video. The framework mainly consists of two stages: object detection and semantic segmentation. In the first stage, an object detection algorithm is used to detect potential intrusion objects. The second stage uses the semantic segmentation algorithm to obtain the railway perimeter area. If the object is found within the railway perimeter, it is regarded as an intrusion. The railway intrusion detection framework proposed in this paper starts the semantic segmentation algorithm only when the object detection algorithm detects potential intrusion objects. Finally, the proposed framework is tested on railway video and achieves 100% accuracy and 23 FPS (Frames Per Second).KeywordsVehicle front videoSemantic segmentationObject detectionRailway intrusion

Recent advancements in deep learning techniques tailored for autonomous driving in Indian road conditions are crucial for revolutionizing transportation systems. Indian roads present unique challenges, including unpredictable traffic patterns, diverse road infrastructures, and challenging weather conditions. Deep learning is pivotal in addressing these challenges, focusing on perception, decision-making, and control. Various architectures, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Deep Reinforcement Learning (DRL), are analyzed for their efficacy in autonomous driving. Sensor fusion techniques enhance perception capabilities, such as lidar, radar, and camera data integration. Additionally, advancements in semantic segmentation and object detection algorithms improve scene understanding and obstacle recognition. Collaborative efforts between academia, industry, and government agencies are essential for accelerating the deployment of autonomous driving technology in Indian road conditions. In conclusion, leveraging cutting-edge deep learning methodologies enables safe and efficient navigation of autonomous vehicles, ushering in a transformative future in transportation systems.

Semantic segmentation is a task that heavily relies on the annotated data. The image annotation cost is very expensive. While active learning aims to select the most valuable samples by an iterative procedure. It can reduce the annotation cost and improve the performance of classification. In semantic segmentation, its more common to select a batch of instances instead of a single instance at each iteration. In this paper, we propose a novel batch mode active learning algorithm for semantic segmentation. Different from the previous active learning algorithms for the image classification, we first introduce a new selecting criterion : the image prior of nonlocal self-similarity. It can measure the interaction between the pixels of the image. We combine the informativeness and representativeness with the criterion of nonlocal self-similarity to complete the selection of images at each iteration. In addition, we also use the model uncertainty to measure the information of samples. The model uncertainty is captured by using Monte Carlo Dropout in the semantic segmentation model. In this work, we evaluate our method on the CamVid and PASCAL VOC 2012 datasets. The importance of the nonlocal self-similarity is also assessed. The experiments demonstrate that our algorithm outperforms current state-of-the-art active learning methods over the segmentation performance.

Efficient data collection of high-quantity and low-cost highway assets such as road signs, traffic signals, light poles, and guardrails is a critical element to the operation, maintenance, and preservation of transportation infrastructure systems. Despite its importance, current practice of highway asset data collection is time-consuming, subjective, and potentially unsafe. The high volume of the data that needs to be collected can also negatively impact the quality of the analysis. To address these limitations, this paper proposes a new algorithm for semantic segmentation and recognition of highway assets using video frames collected from a car-mounted camera. The proposed set of algorithms (1) takes the captured frames and using a pipeline of structure from motion and multiview stereo reconstructs a three-dimensional (3D) point cloud model of the highway and surrounding assets; (2) using a Semantic Texton Forest classifier, each geo-registered two-dimensional (2D) video frame at the pixel-level ...

The segmentation of unstructured roads, a key technology in self-driving technology, remains a challenging problem. At present, most unstructured road segmentation algorithms are based on cameras or use LiDAR for projection, which has considerable limitations that the camera will fail at night, and the projection method will lose one-dimensional information. Therefore, this paper proposes a road boundary enhancement Point-Cylinder Network, called BE-PCFCN, which uses Point-Cylinder in order to extract point cloud features directly and integrates the road enhancement module to achieve accurate unstructured road segmentation. Firstly, we use the improved RANSAC-Boundary algorithm to calculate the rough road boundary point set, training in the same parameters with the original point cloud as a submodule. The whole network adopts the encoder and decoder structure, using Point-Cylinder as the basic module, while considering the data locality and the algorithm complexity. Subsequently, we made an unstructured road data set for training and compared it with existing LiDAR(Light Detection And Ranging) semantic segmentation algorithms. Finally, the experiment verified the robustness of BE-PCFCN. The road intersection-over-union (IoU) was increased by 4% when compared with the best existing algorithm, reaching 95.6%. Even on unstructured roads with an extremely irregular shape, BE-PCFCN also currently has the best segmentation results.

Multi-beam sonar imaging detection technology is increasingly becoming the mainstream technology in fields such as hydraulic safety inspection and underwater target detection due to its ability to generate clearer images under low-visibility conditions. However, during the multi-beam sonar detection process, issues such as low image resolution and blurred imaging edges lead to decreased target segmentation accuracy. Traditional filtering methods for echo signals cannot effectively solve these problems. To address these challenges, this paper introduces, for the first time, a multi-beam sonar dataset against the background of simulated crack detection for dam safety. This dataset included simulated cracks detected by multi-beam sonar from various angles. The width of the cracks ranged from 3 cm to 9 cm, and the length ranged from 0.2 m to 1.5 m. In addition, this paper proposes a BS-UNet semantic segmentation algorithm. The Swin-UNet model incorporates a dual-layer routing attention mechanism to enhance the accuracy of sonar image detail segmentation. Furthermore, an online convolutional reparameterization structure was added to the output end of the model to improve the model’s capability to represent image features. Comparisons of the BS-UNet model with commonly used semantic segmentation models on the multi-beam sonar dataset consistently demonstrated the BS-UNet model’s superior performance, as it improved semantic segmentation evaluation metrics such as Precision and IoU by around 0.03 compared to the Swin-UNet model. In conclusion, BS-UNet can effectively be applied in multi-beam sonar image segmentation tasks.

Chinese spirits is a distilled liquor with a long history, and its quality is largely determined by the quality of the distillation process. Therefore, all liquor enterprises regard distillation as the top priority of the whole brewing process. Traditionally, experienced brewers manually judge the quality of the distillation process, which has a huge and unstable workload, and will seriously affect the production efficiency and quality. This paper collects the image datasets of Chinese spirits in different distillation stages, and achieved 86.8% distillation bubble segmentation recognition rate through label datasets and semantic segmentation algorithm, so as to improve the intelligent level of distillation process discrimination, and make a useful exploration to improve the production efficiency and quality of Chinese spirits. This paper fills the gap of domestic related research.