Bounding Box Research Articles

Enhancing Small Target Detection in Aerial Imagery with Bi-PAN-FPN and EDMOA-Optimized YOLOv8-s

Across the globe, people are working to build "smart cities" that will employ technology to make people's lives better and safer. Installing cameras at strategic spots across the city to monitor public spaces besides provide real-time footage to law enforcement besides other local authorities is a crucial part of smart city infrastructure, which includes video surveillance. A more effective answer is provided by deep learning algorithms, however research in this area still faces significant problems from changes in target size, form change, occlusion, and illumination circumstances as seen from the drone's perspective. In light of the aforementioned issues, this study presents a highly effective and resilient approach for aerial picture identification. To begin, the concept of Bi-PAN-FPN is presented to enhance the neck component of YOLOv8-s, taking into consideration the prevalent issue of small targets being easily misdetected or ignored in aerial photos. We achieve a more advanced and thorough feature fusion procedure much as feasible by completely considering and reusing multiscale features. To further reduce the amount of parameters in the model and prevent info loss during long-distance feature transfer, the benchmark model's backbone incorporates the GhostblockV2 structure in lieu of a portion of the C2f module. With the help of the Enhanced Dwarf Mongoose Optimization Algorithm (EDMOA), the suggested model's hyper-parameters are optimised. Lastly, a dynamic nonmonotonic focusing mechanism is employed in conjunction with WiseIoU loss as bounding box regression loss. The detector accounts for varying anchor box quality by utilizing "outlier" evaluations, thus improving the complete presentation of the detection task.

Crack SAM: enhancing crack detection utilizing foundation models and Detectron2 architecture

Accurate crack detection is crucial for maintaining pavement integrity, yet manual inspections remain labor-intensive and prone to errors, underscoring the need for automated solutions. This study proposes a novel crack segmentation approach utilizing advanced visual models, specifically Detectron2 and the Segment Anything Model (SAM), applied to the CFD and Crack500 datasets, which exhibit intricate and diverse crack patterns. Detectron2 was tested with four configurations—mask_rcnn_R_50_FPN_3x, mask_rcnn_R_101_FPN_3x, faster_rcnn_R_50_FPN_3x, and faster_rcnn_R_101_FPN_3x—while SAM was compared using Focal Loss, DiceCELoss, and DiceFocalLoss. SAM with DiceFocalLoss outperformed Detectron2, achieving mean IoU scores of 0.69 and 0.59 on the CFD and Crack500 datasets, respectively. The integration of Detectron2 with faster_rcnn_R_101_FPN_3x and SAM using DiceFocalLoss involves generating bounding boxes with Detectron2, which serve as prompts for SAM to produce segmentation masks. This approach achieves mIoU scores of 0.83 for CFD dataset and 0.75 for Crack500 dataset. These results highlight the potential of combining foundation models with Detectron2 for advancing crack detection technologies, offering valuable insights for enhancing highway maintenance systems.

Multi-object tracking using score-driven hierarchical association strategy between predicted tracklets and objects

Machine vision is one of the major technologies to guarantee intelligent robots’ human-centered embodied intelligence. Especially in the complex dynamic scene involving multi-person, Multi-Object Tracking (MOT), which can accurately identify and track specific targets, significantly influences intelligent robots’ performance regarding behavior perception and monitoring, autonomous decision-making, and providing personalized humanoid services. In order to solve the problem of targets lost and identity switches caused by the scale variations of objects and frequent overlaps during the tracking process, this paper presents a multi-object tracking method using score-driven hierarchical association strategy between predicted tracklets and objects (ScoreMOT). Firstly, a motion prediction of occluded objects based on bounding box variation (MPOBV) is proposed to estimate the position of occluded objects. MPOBV models the motion state of the object using the bounding box and confidence score. Then, a score-driven hierarchical association strategy between predicted tracklets and objects (SHAS) is proposed to correctly associate them in frequently overlapping scenarios. SHAS associates the predicted tracklets and detected objects with different confidence in different stages. The comparison results with 16 state-of-the-art methods on Multiple Object Tracking Benchmark 20 (MOT20) and DanceTrack datasets are conducted, and ScoreMOT outperforms the compared methods.

Automatic evaluation of Nail Psoriasis Severity Index using deep learning algorithm.

Nail psoriasis is a chronic condition characterized by nail dystrophy affecting the nail matrix and bed. The severity of nail psoriasis is commonly assessed using the Nail Psoriasis Severity Index (NAPSI), which evaluates the characteristics and extent of nail involvement. Although the NAPSI is numeric, reproducible, and simple, the assessment process is time-consuming and often challenging to use in real-world clinical settings. To overcome the time-consuming nature of NAPSI assessment, we aimed to develop a deep learning algorithm that can rapidly and reliably evaluate NAPSI, thereby providing numerous clinical and research advantages. We developed a dataset consisting of 7054 single fingernail images cropped from images of the dorsum of the hands of 634 patients with psoriasis. We annotated the eight features of the NAPSI in a single nail using bounding boxes and trained the YOLOv7-based deep learning algorithm using this annotation. The performance of the deep learning algorithm (DLA) was evaluated by comparing the NAPSI estimated using the DLA with the ground truth of the test dataset. The NAPSI evaluated using the DLA differed by 2 points from the ground truth in 98.6% of the images. The accuracy and mean absolute error of the model were 67.6% and 0.449, respectively. The intraclass correlation coefficient was 0.876, indicating good agreement. Our results showed that the DLA can rapidly and accurately evaluate the NAPSI. The rapid and accurate NAPSI assessment by the DLA is not only applicable in clinical settings, but also provides research advantages by enabling rapid NAPSI evaluations of previously collected nail images.

Unveiling urbanization effects on trees outside forests along the urban-rural gradient in megacity Bengaluru

Rapid urbanization has caused significant changes along the urban-rural gradient, leading to a variety of landscapes that are mainly shaped by human activities. This dynamic interplay also influences the distribution and characteristics of trees outside forests (TOF). Understanding the pattern of these trees will support informed decision-making in urban planning, in conservation strategies, and altogether in sustainable land management practices in the urban context. In this study, we employed a deep learning-based object detection model and high resolution satellite imagery to identify 1.3 million trees with bounding boxes within a 250 ​km2 research transect spanning the urban-rural gradient of Bengaluru, a megacity in Southern India. Additionally, we developed an allometric equation to estimate diameter at breast height (DBH) from the tree crown diameter (CD) derived from the detected bounding boxes. Our study focused on analyzing variations in tree density and tree size along this gradient. The findings revealed distinct patterns: the urban domain displayed larger tree crown diameters (mean: 8.87 ​m) and DBH (mean: 43.78 ​cm) but having relatively low tree density (32 trees per hectare). Furthermore, with increasing distance from the city center, tree density increased, while the mean tree crown diameter and mean tree basal area decreased, showing clear differences of tree density and size between the urban and rural domains in Bengaluru. This study offers an efficient methodology that helps generating instructive insights into the dynamics of TOF along the urban-rural gradient. This may inform urban planning and management strategies for enhancing green infrastructure and biodiversity conservation in rapidly urbanizing cities like Bengaluru.

A helmet detection algorithm based on improved Yolov8n

Abstract To address the issues of false positives and missed detections in current safety helmet algorithms, we propose a new detection method using an enhanced yolov8n model. An enhanced Yolov8n-based algorithm for detecting safety helmets is proposed. In this experiment, a new module called C2f_ECA is proposed. This module can extract channel information and enhance the performance of the network by replacing the original loss function of the algorithm with the EIoU loss function. The aspect ratio loss term is now determined by the difference between the predicted dimensions of width and height and those of the minimum bounding box. This modification speeds up the convergence of predicted boxes and improves their regression accuracy. In addition, a dataset of safety helmets is constructed in complex scenarios. The proposed algorithm enhances Yolov8n’s mAP50 by 1.6% and average accuracy by 1.6% compared to the original and traditional Yolov8 algorithms, respectively. It can efficiently address the issues of false positives and missed detections of safety helmets.

On Boundary Discontinuity in Angle Regression Based Arbitrary Oriented Object Detection.

With vigorous development e.g., in autonomous driving and remote sensing, oriented object detection has gradually been featured. The majority of existing methods directly perform regression on the rotation angle, which we argue has fundamental limitations of boundary discontinuity (even if using Gaussian or RotatedIoU-based losses). In this paper, a novel angle coder named phase-shifting coder (PSC) is proposed to address this issue. Different from another well-explored alternative i.e., angle classification, PSC achieves boundary-discontinuity-free in a continuous and differentiable manner and thus can work together with Gaussian or RotatedIoU-based methods to further boost their performance. Moreover, by rethinking the boundary discontinuity of elongated and square-like objects as rotational symmetry of different cycles, a dual-frequency version (PSCD) is proposed to accurately predict the orientation of both types of objects. Visual analysis and extensive experiments on several popular backbone detectors and datasets demonstrate the effectiveness and the potentiality of our approach. When facing scenarios requiring high-quality bounding boxes, the proposed methods are expected to give a competitive performance.

Search region updating with hierarchical feature fusion for accurate thermal infrared tracking

Due to their resilience against lighting variations, thermal infrared (TIR) images demonstrate robust adaptability in diverse environments, enabling effective object tracking even in intricate scenarios. Nevertheless, TIR target tracking encounters challenges such as fast target motion and interference from visually similar objects, substantially compromising the tracking precision of TIR trackers. To surmount these challenges, we propose a method grounded in the strategy of search region updating and hierarchical feature fusion, tailored for the precise TIR target-tracking task. Specifically, to address the issue of fast motion causing the target to depart from the search region, we propose to update the current search region by leveraging historical frame information. Additionally, we employ a hierarchical feature fusion strategy to contend with interference from visually similar objects in the tracking scenario. This strategy enhances the ability to model and represent the target more accurately, thereby elevating the tracker’s capacity to discriminate between the target and similar objects. Furthermore, to tackle the challenge of inaccurate estimation of target bounding boxes, we introduce an enhanced Intersection over Union (IoU) loss function, which improvement facilitates a more precise prediction of target bounding boxes, resulting in superior target localization. Extensive experiments substantiate that our tracker exhibits a commendable level of competitiveness when compared to other trackers.

Informative Data Selection With Uncertainty for Multimodal Object Detection.

Noise has always been nonnegligible trouble in object detection by creating confusion in model reasoning, thereby reducing the informativeness of the data. It can lead to inaccurate recognition due to the shift in the observed pattern, that requires a robust generalization of the models. To implement a general vision model, we need to develop deep learning models that can adaptively select valid information from multimodal data. This is mainly based on two reasons. Multimodal learning can break through the inherent defects of single-modal data, and adaptive information selection can reduce chaos in multimodal data. To tackle this problem, we propose a universal uncertainty-aware multimodal fusion model. It adopts a multipipeline loosely coupled architecture to combine the features and results from point clouds and images. To quantify the correlation in multimodal information, we model the uncertainty, as the inverse of data information, in different modalities and embed it in the bounding box generation. In this way, our model reduces the randomness in fusion and generates reliable output. Moreover, we conducted a completed investigation on the KITTI 2-D object detection dataset and its derived dirty data. Our fusion model is proven to resist severe noise interference like Gaussian, motion blur, and frost, with only slight degradation. The experiment results demonstrate the benefits of our adaptive fusion. Our analysis on the robustness of multimodal fusion will provide further insights for future research.

Improving the Accuracy of Road Damage Detectors Using Traditional Augmentations and Object Bounding Box Augmentation

Improving the Accuracy of Road Damage Detectors Using Traditional Augmentations and Object Bounding Box Augmentation

Active Learning for Deep Visual Tracking.

Convolutional neural networks (CNNs) have been successfully applied to the single target tracking task in recent years. Generally, training a deep CNN model requires numerous labeled training samples, and the number and quality of these samples directly affect the representational capability of the trained model. However, this approach is restrictive in practice, because manually labeling such a large number of training samples is time-consuming and prohibitively expensive. In this article, we propose an active learning method for deep visual tracking, which selects and annotates the unlabeled samples to train the deep CNN model. Under the guidance of active learning, the tracker based on the trained deep CNN model can achieve competitive tracking performance while reducing the labeling cost. More specifically, to ensure the diversity of selected samples, we propose an active learning method based on multiframe collaboration to select those training samples that should be and need to be annotated. Meanwhile, considering the representativeness of these selected samples, we adopt a nearest-neighbor discrimination method based on the average nearest-neighbor distance to screen isolated samples and low-quality samples. Therefore, the training samples' subset selected based on our method requires only a given budget to maintain the diversity and representativeness of the entire sample set. Furthermore, we adopt a Tversky loss to improve the bounding box estimation of our tracker, which can ensure that the tracker achieves more accurate target states. Extensive experimental results confirm that our active-learning-based tracker (ALT) achieves competitive tracking accuracy and speed compared with state-of-the-art trackers on the seven most challenging evaluation benchmarks. Project website: https://sites.google.com/view/altrack/.

Soft set-based MSER end-to-end system for occluded scene text detection, recognition and prediction

The presence of unpredictable occlusions on natural scene text is a significant challenge, exacerbating the difficulties already posed on text detection and recognition by the variability of such images. Addressing the need for a robust, consistently performing approach that can effectively address the above challenges, this paper presents a new Soft Set-based end-to-end system for text detection, recognition and prediction in occluded natural scene images. This is the first approach to integrate text detection, recognition and prediction, unlike existing systems developed for end-to-end text spotting (text detection and recognition) only. For candidate text components detection, the proposed combination of Soft Sets with Maximally Stable Extremal Regions (SS-MSER) improves text detection and spotting in natural scene images, irrespectively of the presence of arbitrarily orientated and shaped text, complex backgrounds and occlusion. Furthermore, a Graph Recurrent Neural Network is proposed for grouping candidate text components into text lines and for fitting accurate bounding boxes to each word. Finally, a Convolutional Recurrent Neural Network (CRNN) is proposed for the recognition of text and for predicting missing characters due to occlusion. Experimental results on a new occluded scene text dataset (OSTD) and on the most relevant benchmark natural scene text datasets demonstrate that the proposed system outperforms the state-of-the-art in text detection, recognition and prediction. The code and dataset are available at https://github.com/alloydas/Softset-MSER-Based-Occluded-Scene-Text-Spotting/blob/master/Soft_set_MSER.ipynb

Diffusion model conditioning on gaussian mixture model and negative gaussian mixture gradient

Diffusion models (DMs) are a type of generative model that has had a significant impact on image synthesis and beyond.They can incorporate a wide variety of conditioning inputs—such as text or bounding boxes—to guide generation. In this work, we introduce a novel conditioning mechanism that applies Gaussian mixture models (GMMs) for feature conditioning, which helps steer the denoising process in DMs. Drawing on set theory, our comprehensive theoretical analysis reveals that the conditional latent distribution based on features differs markedly from that based on classes. Consequently, feature-based conditioning tends to generate fewer defects than class-based conditioning. Experiments are designed and carried out and the experimental results support our theoretical findings as well as effectiveness of proposed feature conditioning mechanism. Additionally, we propose a new gradient function named the Negative Gaussian Mixture Gradient (NGMG) and incorporate it into the training of diffusion models alongside an auxiliary classifier. We theoretically demonstrate that NGMG offers comparable advantages to the Wasserstein distance, serving as a more effective cost function when learning distributions supported by low-dimensional manifolds, especially in contrast to many likelihood-based cost functions, such as KL divergences.

DCW-YOLO: An Improved Method for Surface Damage Detection of Wind Turbine Blades

Wind turbine blades (WTBs) are prone to damage from their working environment, including surface peeling and cracks. Early and effective detection of surface defects on WTBs can avoid complex and costly repairs and serious safety hazards. Traditional object detection methods have disadvantages of insufficient detection capabilities, extended model inference times, low recognition accuracy for small objects, and elongated strip defects within WTB datasets. In light of these challenges, a novel model named DCW-YOLO for surface damage detection of WTBs is proposed in this research, which leverages image data collected by unmanned aerial vehicles (UAVs) and the YOLOv8 algorithm for image analysis. Firstly, Dynamic Separable Convolution (DSConv) is introduced into the C2f module of YOLOv8, allowing the model to more effectively focus on the geometric structural details associated with damage on WTBs. Secondly, the upsampling method is replaced with the content-aware reassembly of features (CARAFE), which significantly minimizes the degradation of image characteristics throughout the upsampling process and boosts the network’s ability to extract features. Finally, the loss function is substituted with the WIoU (Wise-IoU) strategy. This strategy allows for a more accurate regression of the target bounding boxes and helps to improve the reliability in the localization of WTBs damages, especially for low-quality examples. This model demonstrates a notable superiority in surface damage detection of WTBs compared to the original YOLOv8n and has achieved a substantial improvement in the mAP@0.5 metric, rising from 91.4% to 93.8%. Furthermore, in the more rigorous mAP@0.5–0.95 metric, it has also seen an increase from 68.9% to 71.2%.

A dataset of manually annotated filaments from H-alpha observations.

We present the Manually Annotated GONG Filaments in H-alpha Observations (MAGFiLO v1.0) dataset. This dataset contains 10,244 annotated filaments from 1,593 observations captured by the Global Oscillation Network Group (GONG), spanning the years 2011 through 2022. Each annotation details one filament's segmentation, minimum bounding box, spine, and magnetic field chirality. With a total of over one thousand person-hours of annotation, and a double-blind review process, we ensured high-quality ground-truth data. Our inter-annotator agreement reaches a Kappa score of 0.66. We also verified that the hemispheric preference of filaments as annotated in MAGFiLO aligns with the findings from similar datasets of much smaller sample sizes. MAGFiLO is the first dataset of its size, enabling advanced deep learning models to identify filaments and their features with unprecedented precision. It also provides a testbed for solar physicists interested in large-scale analysis of filaments. In this report, we document the details of the annotation and the post-processing phases that were applied.

Evaluating fine tuned deep learning models for real-time earthquake damage assessment with drone-based images

Earthquakes pose a significant threat to life and property worldwide. Rapid and accurate assessment of earthquake damage is crucial for effective disaster response efforts. This study investigates the feasibility of employing deep learning models for damage detection using drone imagery. We explore the adaptation of models like VGG16 for object detection through transfer learning and compare their performance to established object detection architectures like YOLOv8 (You Only Look Once) and Detectron2. Our evaluation, based on various metrics including mAP, mAP50, and recall, demonstrates the superior performance of YOLOv8 in detecting damaged buildings within drone imagery, particularly for cases with moderate bounding box overlap. This finding suggests its potential suitability for real-world applications due to the balance between accuracy and efficiency. Furthermore, to enhance real-world feasibility, we explore two strategies for enabling the simultaneous operation of multiple deep learning models for video processing: frame splitting and threading. In addition, we optimize model size and computational complexity to facilitate real-time processing on resource-constrained platforms, such as drones. This work contributes to the field of earthquake damage detection by (1) demonstrating the effectiveness of deep learning models, including adapted architectures, for damage detection from drone imagery, (2) highlighting the importance of evaluation metrics like mAP50 for tasks with moderate bounding box overlap requirements, and (3) proposing methods for ensemble model processing and model optimization to enhance real-world feasibility. The potential for real-time damage assessment using drone-based deep learning models offers significant advantages for disaster response by enabling rapid information gathering to support resource allocation, rescue efforts, and recovery operations in the aftermath of earthquakes.

SDCINet: A novel cross-task integration network for segmentation and detection of damaged/changed building targets with optical remote sensing imagery

Buildings are primary locations for human activities and key focuses in the military domain. Rapidly detecting damaged/changed buildings (DCB) and conducting detailed assessments can effectively aid urbanization monitoring, disaster response, and humanitarian assistance. Currently, the tasks of object detection (OD) and change detection (CD) for DCB are almost independent of each other, making it difficult to simultaneously determine the location and details of changes. Based on this, we have designed a cross-task network called SDCINet, which integrates OD and CD, and have created four dual-task datasets focused on disasters and urbanization. SDCINet is a novel deep learning dual-task framework composed of a consistency encoder, differentiation decoder, and cross-task global attention collaboration module (CGAC). It is capable of modeling differential feature relationships based on bi-temporal images, performing end-to-end pixel-level prediction, and object bounding box regression. The bi-direction traction function of CGAC is used to deeply couple OD and CD tasks. Additionally, we collected bi-temporal images from 10 locations worldwide that experienced earthquakes, explosions, wars, and conflicts to construct two datasets specifically for damaged building OD and CD. We also constructed two datasets for changed building OD and CD based on two publicly available CD datasets. These four datasets can serve as data benchmarks for dual-task research on DCB. Using these datasets, we conducted extensive performance evaluations of 18 state-of-the-art models from the perspectives of OD, CD, and instance segmentation. Benchmark experimental results demonstrated the superior performance of SDCINet. Ablation experiments and evaluative analyses confirmed the effectiveness and unique value of CGAC.

Design and Testing of a Fruit Tree Variable Spray System Based on ExG-AABB

This paper addresses the issue of pesticide waste and low utilization rates resulting from traditional plant protection via spraying operations, which apply equal dosages to different targets or to different parts of the same target. To tackle this problem, we designed a variable fruit tree spraying system based on the ExG-AABB (excess green and axis-aligned bounding box) algorithm. We used a Kinect depth camera to capture information about the fruit tree canopy and constructed a spray flow model using pulse width modulation and variable spray control technology. Variable multi-nozzle spraying was guided by combining this canopy data. We evaluated the accuracy of each model in calculating canopy volume by comparing the coefficient of determination (R2) and root mean square error (RMSE) of the ExG-AABB with the slice convex hull method, voxel method, three-dimensional alpha-shape method, and QuickHull method. The ExG-AABB algorithm had the highest R2 value (0.9334) and the lowest RMSE value (0.0353 m3) among the five models, indicating that it most accurately reflects the true volume of the fruit tree canopy. This validates the effectiveness of the ExG-AABB algorithm in calculating canopy volume. We established a correlation model between canopy volume and spray volume, designed a canopy-adaptive layering method based on point cloud processing, and achieved precise calculation of nozzle flow. Comparative field experiments were conducted to analyze the spray coverage rate and observed flow, thereby evaluating the spraying effect of this variable spraying system. The experimental results showed that compared to conventional continuous spraying, this variable spraying system not only achieves more uniform spray coverage but also significantly reduces pesticide usage by 48.1%. Furthermore, through system optimization, the average coverage rate of the middle layer of the canopy decreased by 17.53%, effectively reducing the phenomenon of overlapping spraying from multiple nozzles and improving spraying efficiency.

SOD-YOLOv8-Enhancing YOLOv8 for Small Object Detection in Aerial Imagery and Traffic Scenes.

Object detection, as a crucial aspect of computer vision, plays a vital role in traffic management, emergency response, autonomous vehicles, and smart cities. Despite the significant advancements in object detection, detecting small objects in images captured by high-altitude cameras remains challenging, due to factors such as object size, distance from the camera, varied shapes, and cluttered backgrounds. To address these challenges, we propose small object detection YOLOv8 (SOD-YOLOv8), a novel model specifically designed for scenarios involving numerous small objects. Inspired by efficient generalized feature pyramid networks (GFPNs), we enhance multi-path fusion within YOLOv8 to integrate features across different levels, preserving details from shallower layers and improving small object detection accuracy. Additionally, we introduce a fourth detection layer to effectively utilize high-resolution spatial information. The efficient multi-scale attention module (EMA) in the C2f-EMA module further enhances feature extraction by redistributing weights and prioritizing relevant features. We introduce powerful-IoU (PIoU) as a replacement for CIoU, focusing on moderate quality anchor boxes and adding a penalty based on differences between predicted and ground truth bounding box corners. This approach simplifies calculations, speeds up convergence, and enhances detection accuracy. SOD-YOLOv8 significantly improves small object detection, surpassing widely used models across various metrics, without substantially increasing the computational cost or latency compared to YOLOv8s. Specifically, it increased recall from 40.1% to 43.9%, precision from 51.2% to 53.9%, mAP0.5 from 40.6% to 45.1%, and mAP0.5:0.95 from 24% to 26.6%. Furthermore, experiments conducted in dynamic real-world traffic scenes illustrated SOD-YOLOv8's significant enhancements across diverse environmental conditions, highlighting its reliability and effective object detection capabilities in challenging scenarios.

2L-LSH: A Locality-Sensitive Hash Function-Based Method For Rapid Point Cloud Indexing

Abstract The development of 3D scanning technology has enabled the acquisition of massive point cloud models with diverse structures and large scales, thereby presenting significant challenges in point cloud processing. Fast neighboring points search is one of the most common problems, which is frequently used in model reconstruction, classification, retrieval and feature visualization. Hash function is well known for its high-speed and accurate performance in searching high-dimensional data, which is also the core of the proposed 2L-LSH. Specifically, the 2L-LSH algorithm adopts a two-step hash function strategy, in which the popular step divides the bounding box of the point cloud model and the second step constructs a generalized table-based data structure. The proposed 2L-LSH offers a highly efficient and accurate solution for fast neighboring points search in large-scale 3D point cloud models, making it a promising technique for various applications in the field. The proposed algorithm is compared with the well-known methods including Kd-tree and Octree; the obtained results demonstrated that the proposed method outperforms Kd-tree and Octree in terms of speed, i.e. the time consumption of kNN search can be 51.111% and 94.159% lower than Kd-tree and Octree, respectively. And the RN search time can be 54.519% and 41.840% lower than Kd-tree and Octree, respectively.