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Online segmentation of street trees from mobile laser scanning data via deep learning image instance segmentation

OPTICS AND LASER TECHNOLOGY 2025年 184卷

作者： Li, Qiujie Gao, Junjie Nanjing Forestry Univ Coll Mech & Elect Engn Nanjing 210037 Peoples R China

A mobile laser scanning (MLS) system can efficiently collect the vertical structure information of street trees through light detection and ranging (LiDAR) movement. Segmenting street tree instances from street MLS data is the primary step in conducting street tree inventories at the single-tree scale, and existing methods are limited by the high computational complexity of three-dimensional (3D) point cloud processing, resulting in low speed and not supporting real-time applications. This paper proposes an efficient and effective online framework for segmenting street trees from street MLS data via deep learning image instance segmentation. First, the point cloud data collected in real time by LiDAR are stored in a first-in-first-out (FIFO) buffer. When the number of new LiDAR frames reaches the set segmentation step, the point cloud data in the FIFO buffer are converted into a three-channel image using point-pixel mapping. Next, a trained high-performance YOLOv8 segmentation model is used to segment an image instance of the street tree quickly. Then, a point cloud instance of the street tree is accurately segmented through optimization at the pixel and point levels. Finally, the segmentation step is adaptively adjusted on the basis of the street tree image instance segmentation result to improve the online processing efficiency. During the experiment, an MLS system equipped with a UTM-30LX-EW sensor is used to collect one-sided street point cloud data containing 149 street trees and other objects, such as landscape plants, buildings, viaducts, lanes, sidewalks, street lights, traffic signs, trash cans, baffles, fences, cars, motorcycles, and pedestrians. Eight YOLOv8 models with different target image sizes are compared, and the 416 x 320 model is selected to balance segmentation accuracy and speed, with an AP90 of 0.9950 and a segmentation time per image of 16.81 ms. Through pixel- and point-level optimization, the F1 score for online segmentation of street tree

关键词： Online segmentation Street tree Mobile laser scanning image instance segmentation Point cloud instance segmentation Adaptive segmentation step

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Street tree segmentation from mobile laser scanning data using deep learning-based image instance segmentation

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URBAN FORESTRY & URBAN GREENING 2024年 92卷

作者： Li, Qiujie Yan, Yu Nanjing Forestry Univ Nanjing Peoples R China

Street tree inventory is an important part of urban forest inventory. Mobile laser scanning (MLS) technology has a strong data acquisition ability inside the canopy and the trunk and is suitable for parameter estimation at the tree level. The first and key step is to segment individual trees from the street MLS data, which is a challenging problem due to the semantic gap. This paper proposes an efficient and effective method for street tree segmentation from MLS data using deep learning -based image instance segmentation. First, the three-dimensional (3D) street point cloud captured by the MLS system is mapped to a two-dimensional (2D) RGB image. Next, pixelwise tree proposals are segmented from the street image by a trained deep learning -based image instance segmentation model. Then, the 2D segmentation mask is mapped back to the 3D street point cloud to generate pointwise tree proposals. Finally, the proposals are optimized to obtain the final results. To evaluate and verify algorithm performance, an MLS point cloud is collected from a 1481.8 m -long one -side urban street containing various objects. Three deep learning -based image instance segmentation algorithms, YOLACT, BlendMask, and YOLOv8, are carried out, and YOLOv8 achieves the best results in terms of both accuracy and speed. YOLOv8 has the highest segmentation accuracy, with IoU= 0.85:0.05:0.95, with an average segmentation time of 26 ms per image. In the experiments comparing the algorithms to the existing hierarchical segmentation and classification segmentation methods, the proposed method outperforms the other two methods in accuracy and is faster. The precision is 0.9988, the recall is 0.9986, the F1 score is 0.9987, and the time per scanline is 4.05 ms. Moreover, the proposed method can be applied to MLS data with a broad range of resolutions by introducing image resizing.

关键词： Street tree segmentation Mobile laser scanning image instance segmentation Point cloud instance segmentation Deep learning

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Infrared Object image instance segmentation based on Improved Mask-RCNN 6

Infrared Object Image Instance Segmentation based on Improve...

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Conference on Optoelectronic Imaging and Multimedia Technology VI

作者： Nan Jing Bo Lei Huazhong Inst Electroopt Wuhan Natl Lab Optoelect 717th Yangguang Ave Wuhan Hubei Peoples R China

ISBN: (数字)9781510630925

ISBN: (纸本)9781510630925

In recent years, the traditional automotive industry has also begun to enter the field of autonomous driving technology, seeking new breakthroughs. From simple pedestrian and vehicle detection, to the instance segmentation of traffic scenes, to the ideal all-intelligent driving, it has begun to be occupied by deep learning. Research scholars have tried to build a vehicle's control strategy system entirely using computers and also proposed a full convolutional neural network that replaced the fully connected structure with convolutional ideas, realizing the transition from image classification to dense pixel prediction. This is the first step for neural network to be used for scene instance segmentation, and it is also a key step for intelligent driving. However, the effect of the full convolutional neural network is not very ideal. An important problem is that the pooling layer will lose part of the location information while aggregating the background. For dense prediction of image instance segmentation, the context information (location information) of each pixel is indispensable, which is very important for the final classification of pixels. Thus, later researchers proposed three different structures to solve this problem: cavity convolution structure, codec structure, space pyramid structure. This paper analyzes the working principles and characteristics of several different structures and compares the differences between various networks. This paper combines a Mask-RCNN to construct a new network structure for image instance segmentation. The main innovations of this paper are as follows: 1. Introduce generative adversarial network into the field of image segmentation. Combine the conditional generative adversarial network idea, using the original image as the input of the generator, and the generative to generate the desired instance segmentation result. Combine the original image with the instance segmentation result generated by the generative, or combine t

关键词： image instance segmentation Mask-RCNN ResNet Infrared object

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SipMaskv2: Enhanced Fast image and Video instance segmentation

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2023年第3期45卷 3798-3812页

作者： Cao, Jiale Pang, Yanwei Anwer, Rao Muhammad Cholakkal, Hisham Khan, Fahad Shahbaz Shao, Ling Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China Mohamed bin Zayed Univ Artificial Intelligence Abu Dhabi U Arab Emirates Linkoping Univ S-58183 Linkoping Sweden Terminus Grp Beijing 100027 Peoples R China

We propose a fast single-stage method for both image and video instance segmentation, called SipMask, that preserves the instance spatial information by performing multiple sub-region mask predictions. The main module in our method is a light-weight spatial preservation (SP) module that generates a separate set of spatial coefficients for the sub-regions within a bounding-box, enabling a better delineation of spatially adjacent instances. To better correlate mask prediction with object detection, we further propose a mask alignment weighting loss and a feature alignment scheme. In addition, we identify two issues that impede the performance of single-stage instance segmentation and introduce two modules, including a sample selection scheme and an instance refinement module, to address these two issues. Experiments are performed on both image instance segmentation dataset MS COCO and video instance segmentation dataset YouTube-VIS. On MS COCO test-dev set, our method achieves a state-of-the-art performance. In terms of real-time capabilities, it outperforms YOLACT by a gain of 3.0% (mask AP) under the similar settings, while operating at a comparable speed. On YouTube-VIS validation set, our method also achieves promising results. The source code is available at https://***/JialeCao001/SipMask.

关键词： image segmentation Training Real-time systems Proposals Feature extraction Object detection Task analysis image instance segmentation video instance segmentation real-time single-stage method spatial information preservation

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instance segmentation of faces and mouth-opening degrees based on improved YOLOv8 method

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MULTIMEDIA SYSTEMS 2024年第5期30卷 1-22页

作者： Fan, Yuhe Zhang, Lixun Zheng, Canxing Wang, Xingyuan Zhu, Jinghui Wang, Lan Harbin Engn Univ Coll Mech & Elect Engn Bldg 61Nantong St 145 Harbin 150001 Peoples R China Weifang Peoples Hosp Weifang Shandong Peoples R China

instance segmentation of faces and mouth-opening degrees is an important technology for meal-assisting robotics in food delivery safety. However, due to the diversity in in shape, color, and posture of faces and the mouth with small area contour, easy to deform, and occluded, it is challenging to real-time and accurate instance segmentation. In this paper, we proposed a novel method for instance segmentation of faces and mouth-opening degrees. Specifically, in backbone network, deformable convolution was introduced to enhance the ability to capture finer-grained spatial information and the CloFormer module was introduced to improve the ability to capture high-frequency local and low-frequency global information. In neck network, classical convolution and C2f modules are replaced by GSConv and VoV-GSCSP aggregation modules, respectively, to reduce the complexity and floating-point operations of models. Finally, in localization loss, CIOU loss was replaced by WIOU loss to reduce the competitiveness of high-quality anchor frames and mask the influence of low-quality samples, which in turn improves localization accuracy and generalization ability. It is abbreviated as the DCGW-YOLOv8n-seg model. The DCGW-YOLOv8n-seg model was compared with the baseline YOLOv8n-seg model and several state-of-the-art instance segmentation models on datasets, respectively. The results show that the DCGW-YOLOv8n-seg model is characterized by high accuracy, speed, robustness, and generalization ability. The effectiveness of each improvement in improving the model performance was verified by ablation experiments. Finally, the DCGW-YOLOv8n-seg model was applied to the instance segmentation experiment of meal-assisting robotics. The results show that the DCGW-YOLOv8n-seg model can better realize the instance segmentation effect of faces and mouth-opening degrees. The novel method proposed can provide a guiding theoretical basis for meal-assisting robotics in food delivery safety and can provide

关键词： Computer vision image instance segmentation Meal-assisting robotics Faces and mouth-opening degrees YOLOv8

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Feature attention based detection model for medical text

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2019年第4期37卷 4585-4594页

作者： Xie, Qubo Zhou, Ke Fu, Xiao Fan, Xiaohu Huazhong Univ Sci & Technol Wuhan Hubei Peoples R China Wuhan Yu Ran Intelligent Technol Co Ltd Wuhan Hubei Peoples R China

Recently, many breakthroughs have been achieved in the text detection field;however, printed text detection performance remains unsatisfactory. To address this issue, this paper proposes a refined feature attention based text detection model comprising a feature attention FCNs and text instance segmentation. With the feature attention mechanism, the FCNs model is optimized effectively, which enables the network to learn more precise and accurate features. Therefore, the network can better detect noise-intensive and dense text in medical images. In addition, a centerline-based text region detection algorithm is proposed to process the output of network during text instance segmentation. This algorithm calculates each text region according to the geometric information of the text instance;thus, it is able to process multi-oriented text instances precisely. The proposed model can be trained end-to-end and does not require post-processing operations, which greatly increases detection efficiency. The proposed model achieved excellent results on a medical text image dataset. Compared to existing text detection models, the proposed model demonstrates significantly better performance in terms of F-meatures and detection speed.

关键词： Printed text detection attention mechanism medical text image image instance segmentation

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The segmentation Algorithm of Marine Warship image Based on Mask RCNN 12

The Segmentation Algorithm of Marine Warship Image Based on ...

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12th International Conference on Signal Processing Systems

作者： Chen, Hongyan Yu, Xinle Commun Univ China Beijing Peoples R China

ISBN: (纸本)9781510642768;9781510642751

This paper proposes a warship image segmentation algorithm based on Mask RCNN network. Based on the Tensorflow+Keral deep learning framework, the Mask-RCNN network structure was constructed. The segmentation of the image of warship at sea level was achieved by using the supervised learning method and tagging of the data set. Mask R-CNN is the most advanced convolutional neural network algorithm, which is mainly used for object detection and object instance segmentation of natural images. Due to the difficulty in obtaining warship samples and the insufficient number of data sets, the method of data enhancement is adopted to expand the data set. Through parameter adjustment and experimental verification, the mAP of warship reaches 0.603, which can meet the requirements of high-precision segmentation. The experimental results show that the Mask RCNN model has a very good effect on the image segmentation of naval ships at sea.

关键词： Mask R-CNN image instance segmentation Data to enhance Res Net JPEG2000

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DETECTION OF DIFFERENT CROP GROWTH STAGES BY APPLYING DEEP LEARNING OVER SENTINEL-2 imageS OF BAHIA, BRAZIL

DETECTION OF DIFFERENT CROP GROWTH STAGES BY APPLYING DEEP L...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Camacho-De Angulo, Yineth Viviana Arrechea-Castillo, Darwin Alexis Cantero-Mosquera, Yessica Carolina Solano-Correa, Yady Tatiana Roisenberg, Mauro Univ Fed Santa Catarina BR-88040900 Florianopolis SC Brazil Univ Tecnol Bolivar Cartagena 130010 Colombia Int Ctr Trop Agr Amer Hub Palmira Palmira 763537 Colombia Univ Cauca Popayan 190001 Colombia

ISBN: (纸本)9798350360332;9798350360325

This study delves into the agricultural landscape of Bahia, Brazil, employing the Mask R-CNN deep learning model with satellite imagery to detect three crop growth stages (early, mid-growth and maturity stage). This model is suited to the region's complex terrain and diverse crop patterns, providing accurate instance segmentation crucial for monitoring crop development. Remarkable results have been achieved with a limited dataset of just 54 images for training, underscoring the model's efficiency in scenarios where extensive data collection is challenging. The validation metric chosen for this study is the Intersection over Union (IoU), preferred for its ability to quantify the pixel-wise overlap between the predicted and actual segmentations, offering a clear measure of accuracy in spatial contexts. An IoU of 90% was obtained, demonstrating Mask R-CNN's robustness and potential for precision agriculture in challenging environments.

关键词： Crops Growth Deep Learning image instance segmentation Remote Sensing Mask R-CNN

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Industrial Fisheye image segmentation Using Neural Networks 19th

Industrial Fisheye Image Segmentation Using Neural Networks

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19th International Conference on Computational Science and Its Applications (ICCSA)

作者： Beloshapko, A. Korkhov, V Knoll, C. Iben, U. St Petersburg State Univ 7-9 Univ Skaya Nab St Petersburg 199034 Russia Robert Bosch GmbH Robert Bosch Campus 1 D-71272 Renningen Germany

ISBN: (纸本)9783030243050

Fisheye cameras have recently became very popular in computer vision applications due to their wide field of view. In addition to a better overview of the surrounding area, they enable to capture objects at extremely close ranges. These advantages come at a cost of strong image distortion, which cannot be removed completely maintaining image continuity. This complicates the use of traditional computer vision algorithms, which expect a single image as an input. This paper presents a performance evaluation of neural network algorithms for object detection and segmentation on fisheye camera images. Three approaches are evaluated: semantic image segmentation with Fully Convolutional Network (FCN) [13], a fully convolutional approach to instance segmentation with U-Net [18] and a region-based approach to instance segmentation with Mask R-CNN [10]. All of these networks successfully solved the task. However, as they were designed to different purposes, each of them has its own strengths and shortcomings. These three approaches are used to perform euro container image segmentation task. An image dataset was created in order to train and evaluate these algorithms. Huge part of this dataset was generated artificially, which simplified the task of ground truth labeling. The power of neural networks enable for fast and reliable image segmentation. As to our knowledge, this is the first neural networks application for euro container fisheye image detection and segmentation.

关键词： Fisheye cameras Euro container detection image semantic segmentation image instance segmentation FCN Mask R-CNN U-Net

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Evaluation of Deep Learning-Based segmentation Methods for Industrial Burner Flames

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ENERGIES 2021年第6期14卷 1716-1716页

作者： Grosskopf, Julius Matthes, Jorg Vogelbacher, Markus Waibel, Patrick Karlsruhe Inst Technol Inst Automat & Appl Informat D-76344 Eggenstein Leopoldshafen Germany Kistler Grp Competence Ctr Vis Syst D-76131 Karlsruhe Germany

The energetic usage of fuels from renewable sources or waste material is associated with controlled combustion processes with industrial burner equipment. For the observation of such processes, camera systems are increasingly being used. With additional completion by an appropriate image processing system, camera observation of controlled combustion can be used for closed-loop process control giving leverage for optimization and more efficient usage of fuels. A key element of a camera-based control system is the robust segmentation of each burners flame. However, flame instance segmentation in an industrial environment imposes specific problems for image processing, such as overlapping flames, blurry object borders, occlusion, and irregular image content. In this research, we investigate the capability of a deep learning approach for the instance segmentation of industrial burner flames based on example image data from a special waste incineration plant. We evaluate the segmentation quality and robustness in challenging situations with several convolutional neural networks and demonstrate that a deep learning-based approach is capable of producing satisfying results for instance segmentation in an industrial environment.

关键词： flame segmentation image instance segmentation afterburner chamber combustion process control multi-fuel swirl burner industrial automation

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