Evolutionary segment selection for higher-order conditional random fields in semantic image segmentation

In this study, we propose a segmentation model based on convolutional neural networks (CNNs) to address image segmentation challenges in computer vision. Prior to designing the model, the activation function and other modules of the convolutional neural network were optimized to meet specific requirements. The segmentation task was transformed into binary classification problem to simplify network calculations and improve efficiency. Additionally, the model utilized a mask map obtained from the semantic segmentation model to aid in instance segmentation. Class activation technology was introduced to extract feature mapping maps. The corresponding thermal maps were obtained to achieve target instance segmentation. To further validate the effectiveness of the segmentation model, simulation experiments were conducted on semantic segmentation and instance segmentation respectively. The results show that the accuracy of the basic semantic segmentation model reached 87.58%, while the average accuracy of the entire class of the optimized instance segmentation model reached 97.9%. Therefore, the research and design of image segmentation models demonstrate high accuracy and good robustness.

Methods and datasets on semantic segmentation for Unmanned Aerial Vehicle remote sensing images: A review

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.

The field of continual deep learning is an emerging field and a lot of progress has been made. However, concurrently most of the approaches are only tested on the task of image classification, which is not relevant in the field of intelligent vehicles. Only recently approaches for class-incremental semantic segmentation were proposed. However, all of those approaches are based on some form of knowledge distillation. At the moment there are no investigations on replay-based approaches that are commonly used for object recognition in a continual setting. At the same time while unsupervised domain adaption for semantic segmentation gained a lot of traction, investigations regarding domain-incremental learning in an continual setting is not well-studied. Therefore, the goal of our work is to evaluate and adapt established solutions for continual object recognition to the task of semantic segmentation and to provide baseline methods and evaluation protocols for the task of continual semantic segmentation. We firstly introduce evaluation protocols for the class- and domain-incremental segmentation and analyze selected approaches. We show that the nature of the task of semantic segmentation changes which methods are most effective in mitigating forgetting compared to image classification. Especially, in class-incremental learning knowledge distillation proves to be a vital tool, whereas in domain-incremental learning replay methods are the most effective method.

Uncrewed aerial vehicles (UAVs) aerial remote sensing (RS) images offer higher spatial resolution compared with satellite RS images, providing more detailed scene information. The flexibility of UAV-mounted sensors allows for the simultaneous acquisition of multimodal data, including RGB images and depth maps. Depth information, serving as a supplementary cue, has been extensively utilized in semantic segmentation research. Nevertheless, the current investigations on red, green, blue-depth (RGB-D) image semantic segmentation predominantly concentrate on indoor or road scenarios. In UAV RS scenes, there exist numerous objects featuring similar visual texture manifestations. The introduction of depth geometric information can efficaciously enhance the semantic segmentation accuracy of complex ground objects. Concurrently, high-resolution RS images also present challenges to the modeling of global information in large-scale space. To address these challenges, we propose the first framework for semantic segmentation of RGB-D images in UAV RS, namely, SkyMamba. This framework effectively integrates visual texture information and geometric depth information. Simultaneously, it leverages the long-sequence modeling capability of the state-space model to efficiently capture global context information in the RS space, achieving efficient semantic segmentation of RS scenes. Furthermore, we introduce a novel benchmark dataset for semantic segmentation of UAV RS RGB-D images, named comprehensive urban-scale depth, which is divided into two versions based on coarse-grained and fine-grained semantic annotation strategies to provide diverse challenges. Experimental results demonstrate that the proposed method exhibits state-of-the-art performance on both versions of the dataset, particularly in handling imbalanced scene semantic class distributions. The code for this article is available at the following URL: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/CapyLin-G/CUS-Depth</uri>

Semantic segmentation and activity classification are key components to create intelligent surgical systems able to understand and assist clinical workflow. In the operating room, semantic segmentation is at the core of creating robots aware of clinical surroundings, whereas activity classification aims at understanding OR workflow at a higher level. State-of-the-art semantic segmentation and activity recognition approaches are fully supervised, which is not scalable. Self-supervision can decrease the amount of annotated data needed. We propose a new 3D self-supervised task for OR scene understanding utilizing OR scene images captured with ToF cameras. Contrary to other self-supervised approaches, where handcrafted pretext tasks are focused on 2D image features, our proposed task consists of predicting relative 3D distance of image patches by exploiting the depth maps. By learning 3D spatial context, it generates discriminative features for our downstream tasks. Our approach is evaluated on two tasks and datasets containing multiview data captured from clinical scenarios. We demonstrate a noteworthy improvement in performance on both tasks, specifically on low-regime data where utility of self-supervised learning is the highest. We propose a novel privacy-preserving self-supervised approach utilizing depth maps. Our proposed method shows performance on par with other self-supervised approaches and could be an interesting way to alleviate the burden of full supervision.

In recent years, codetecting objects through the use of contextual information across multiple images has attracted considerable attention. We introduce an object codetection method that exploits contextual information among multiple images through a higher-order conditional random field (CRF). First, we obtain object candidates from each image of a test set by using a pretrained detector. Second, we feed the object candidates into a higher-order CRF that captures the appearance similarity using pairwise potentials and object category co-occurrence constraints using higher-order potentials. Finally, we jointly predict the category labels of all object candidates through the mean field inference in the CRF. Experimental results on the Caltech Pedestrian, PASCAL VOC 2007, PASCAL VOC 2012, and COCO datasets demonstrate the effectiveness of the proposed method compared to the baseline method.

Preparation of high-quality datasets for the urban scene understanding is a labor-intensive task, especially, for datasets designed for the autonomous driving applications. The application of the coarse ground truth (GT) annotations of these datasets without detriment to the accuracy of semantic image segmentation (by the mean intersection over union - mIoU) could simplify and speedup the dataset preparation and model fine tuning before its practical application. Here the results of the comparative analysis for semantic segmentation accuracy obtained by PSPNet deep learning architecture are presented for fine and coarse annotated images from Cityscapes dataset. Two scenarios were investigated: scenario 1 - the fine GT images for training and prediction, and scenario 2 - the fine GT images for training and the coarse GT images for prediction. The obtained results demonstrated that for the most important classes the mean accuracy values of semantic image segmentation for coarse GT annotations are higher than for the fine GT ones, and the standard deviation values are vice versa. It means that for some applications some unimportant classes can be excluded and the model can be tuned further for some classes and specific regions on the coarse GT dataset without loss of the accuracy even. Moreover, this opens the perspectives to use deep neural networks for the preparation of such coarse GT datasets.

Abstract. Although deep learning has greatly improved the semantic segmentation accuracy of point clouds, the segmentation of rare classes in large-scale urban scenes has not been targeted in available methods. This paper proposes a two-stage segmentation framework with automated workflows for imbalanced rare classes based on general semantic segmentation. The proposed approach includes two stages: general semantic segmentation and object-based refined semantic segmentation. Firstly, general segmentation networks are utilized to segment general large objects. Secondly, refined semantic segmentation is conducted by an automated workflow: 3D clustering and bounding box (BBox) generation are applied to the point cloud of rare fine-grained objects during the training, followed by object detection to extract fine-grained objects. Afterwards, as the constraints, the extracted BBoxes further refine the segmentation results. Our approach is evaluated on the Hessigheim High-Resolution 3D Point Cloud (H3D) Benchmark and obtains state-of-the-art 89.35% overall accuracy and outstanding 75.70% mean F1-Score. Furthermore, rare classes Vehicle and Chimney achieve breakthroughs from zero to 63.63% and 52.00% in F1-score, respectively.

Multimodality semantic segmentation based on polarization and color images

Adaptive path planning for UAVs for multi-resolution semantic segmentation

In this paper, we address the problems of contour detection, bottom-up grouping, object detection and semantic segmentation on RGB-D data. We focus on the challenging setting of cluttered indoor scenes, and evaluate our approach on the recently introduced NYU-Depth V2 (NYUD2) dataset (Silberman et al., ECCV, 2012). We propose algorithms for object boundary detection and hierarchical segmentation that generalize the $$gPb-ucm$$gPb-ucm approach of Arbelaez et al. (TPAMI, 2011) by making effective use of depth information. We show that our system can label each contour with its type (depth, normal or albedo). We also propose a generic method for long-range amodal completion of surfaces and show its effectiveness in grouping. We train RGB-D object detectors by analyzing and computing histogram of oriented gradients on the depth image and using them with deformable part models (Felzenszwalb et al., TPAMI, 2010). We observe that this simple strategy for training object detectors significantly outperforms more complicated models in the literature. We then turn to the problem of semantic segmentation for which we propose an approach that classifies superpixels into the dominant object categories in the NYUD2 dataset. We design generic and class-specific features to encode the appearance and geometry of objects. We also show that additional features computed from RGB-D object detectors and scene classifiers further improves semantic segmentation accuracy. In all of these tasks, we report significant improvements over the state-of-the-art.

Hyperspectral remote sensing images have been shown to be particularly beneficial for detecting the types of materials in a scene due to their unique spectral properties. This paper proposes a novel semantic segmentation method for hyperspectral image (HSI), which is based on a new spatial-spectral filtering, called extended extrema morphological profiles (EEMPs). Firstly, principal component analysis (PCA) is used as the feature extractor to construct the feature maps by extracting the first informative feature from the hyperspectral image (HSI). Secondly, the extrema morphological profiles (EMPs) are used to extract the spatial-spectral feature from the informative feature maps to construct the EEMPs. Finally, support vector machine (SVM) is utilized to obtain accurate semantic segmentation from the EEMPs. In order to evaluate the semantic segmentation results, the proposed method is tested on a widely used hyperspectral dataset, i.e., Houston dataset, and four metrics, i.e., class accuracy (CA), overall accuracy (OA), average accuracy (AA), and Kappa coefficient, are used to quantitatively measure the segmentation accuracy. The experimental results demonstrate that EEMPs can efficiently achieve good semantic segmentation accuracy.

Detecting oil palm plantation from high-resolution satellite images can provide the necessary information for palm oil production estimation and oil palm plantation layout planning, etc. In this paper, we proposed a novel semantic segmentation based approach for large-scale oil palm plantation detection using QuickBird images and Google Earth Images (in 0.6-m spatial resolution) in Malaysia. We manually labeled a dataset for pixel-wise semantic segmentation into four categories: oil palm plantation, other vegetation, impervious/cloud, and the others (e.g. water and uncertain pixels). We presented an end-to-end deep convolutional neural network (DCNN) for semantic segmentation followed by fully connected conditional random fields (CRF) and applied an ensemble learning method to improve the localization of boundaries. The overall accuracy and mean IoU of our proposed approach in test regions are 95.27% and 88.46%, which are greatly better than the results of the other three common semantic segmentation methods and patch-based CNN method.

In recent years, brain tumor has become a severe threat to human lives. These tumors are so often inadequately contrasted and are inadequately dispersed. In recent days, brain tumor is automatically detected using semantic segmentation. However, the variability in the size of brain tumors and the low contrast of brain imaging are the two major problems affecting the performance of semantic segmentation. To address this problem, a squirrel search algorithm-based deep convolution neural network (SSA-DCNN) proposed for semantic segmentation of the medical images in this paper. The proposed method is a blend of deep convolution neural network (DCNN) and squirrel search algorithm (SSA). The SSA is used to fine-tune the performance of DCNN by optimizing the hyperparameters of the DCNN, which in turn enhances the accuracy of the semantic segmentation. The proposed method is implemented and validated by performance metrics such as accuracy, loss, IoU, and BF score. The performance of SSA-DCNN is compared with the jellyfish algorithm-based deep convolution neural network (JA-DCNN), oppositional-based seagull optimization algorithm (OSOA-3DCNN), and particle swarm optimization (PSO)-DCNN.KeywordsSquirrel searchDeep convolution networkBrain tumorSemantic segmentation