Object Detection Research Articles

ENHANCING CNN PERFORMANCE WITH RIGHT-NEIGHBOR DEVIATION (RND) POOLING: A FOCUS ON MULTI-CLASS BRAIN TUMOR CLASSIFICATION IN MRI IMAGES

In recent years, the field of image processing and computer vision has been deeply transformed by the advent of Convolutional Neural Network (CNN). These deep learning models, inspired by biological processes, have demonstrated remarkable success in tasks such as image classification, object detection, and semantic segmentation. A critical component of this success is feature extraction, where the pooling layer within CNN serves as a nonlinear down-sampling technique. This layer reduces the spatial size of the convolved feature map through operations like average or maximum pooling, significantly impacting the model’s performance in complex tasks. This research introduces a novel pooling algorithm, Right-Neighbor Deviation (RND), designed to enhance the performance of CNN. The RND pooling method aims to capture detailed spatial features and hierarchical representations more effectively, thereby improving the accuracy and resilience of deep learning models. A dataset of multi-class brain tumor classification, incorporating MRI images from the BR35H dataset, is utilized to explore the potential benefits of RND pooling compared to traditional methods. The effectiveness of the proposed method is evaluated using performance metrics including accuracy, precision, recall, and F1 score, achieving generalized values of 98.86%, 98.84%, 98.80%, and 98.80%, respectively. The results highlight the potential of RND pooling to significantly advance the state of the art in brain tumor classification using CNNs, potentially leading to improved diagnosis and treatment outcomes.

End-to-end multiple object tracking in high-resolution optical sensors of drones with transformer models

Drone aerial imaging has become increasingly important across numerous fields as drone optical sensor technology continues to advance. One critical challenge in this domain is achieving both accurate and efficient multi-object tracking. Traditional deep learning methods often separate object identification from tracking, leading to increased complexity and potential performance degradation. Conventional approaches rely heavily on manual feature engineering and intricate algorithms, which can further limit efficiency. To overcome these limitations, we propose a novel Transformer-based end-to-end multi-object tracking framework. This innovative method leverages self-attention mechanisms to capture complex inter-object relationships, seamlessly integrating object detection and tracking into a unified process. By utilizing end-to-end training, our approach simplifies the tracking pipeline, leading to significant performance improvements. A key innovation in our system is the introduction of a trajectory detection label matching technique. This technique assigns labels based on a comprehensive assessment of object appearance, spatial characteristics, and Gaussian features, ensuring more precise and logical label assignments. Additionally, we incorporate cross-frame self-attention mechanisms to extract long-term object properties, providing robust information for stable and consistent tracking. We further enhance tracking performance through a newly developed self-characteristics module, which extracts semantic features from trajectory information across both current and previous frames. This module ensures that the long-term interaction modules maintain semantic consistency, allowing for more accurate and continuous tracking over time. The refined data and stored trajectories are then used as input for subsequent frame processing, creating a feedback loop that sustains tracking accuracy. Extensive experiments conducted on the VisDrone and UAVDT datasets demonstrate the superior performance of our approach in drone-based multi-object tracking.

GLSD: a global large-scale ship database with baseline evaluations

ABSTRACT In this paper, we introduce a challenging Global Large-scale Ship Database (GLSD), designed specifically for ship detection tasks. The designed GLSD database includes a total of 212,357 annotated instances from 152,576 images. Based on the collected images, we propose 13 ship categories that widely exist in international routes. These categories include Sailing boat, Fishing boat, Passenger ship, Warship, General cargo ship, Container ship, Bulk cargo carrier, Barge, Ore carrier, Speed boat, Canoe, Oil carrier, and Tug. The motivations for developing GLSD include the following: 1) providing a refined and extensive ship detection database that benefits the object detection community, 2) establishing a database with exhaustive labels (bounding boxes and ship class categories) in a uniform classification scheme, and 3) providing a large-scale ship database with geographic information (covering more than 3000 ports and 33 routes) that benefits multi-modal analysis. Additionally, we discuss the evaluation protocols corresponding to image characteristics in GLSD and analyze the performance of selected state-of-the-art object detection algorithms on GLSD, aiming to establish baselines for future studies.

Improved Real-Time Detection Transformer-Based Rail Fastener Defect Detection Algorithm

To address the issues of the Real-Time DEtection TRansformer (RT-DETR) object detection model, including poor defect feature extraction in the task of rail fastener defect detection, inefficient use of computational resources, and suboptimal channel attention in the self-attention mechanism, the following improvements were made. Firstly, a Super-Resolution Convolutional Module (SRConv) was designed as a separate component and integrated into the Backbone network, which enhances the image details and clarity while preserving the original image structure and semantic content. This integration improves the model’s ability to extract defect features. Secondly, a channel attention mechanism was integrated into the self-attention module of RT-DETR to enhance the focus on feature map channels, addressing the problem of sparse attention maps caused by the lack of channel attention while saving computational resources. Finally, the experimental results show that compared to the original model, the improved RT-DETR-based rail fastener defect detection algorithm, with an additional 0.4 MB of parameters, achieved a higher accuracy, with a 2.8 percentage point increase in the Mean Average Precision (mAP) across IoU thresholds from 0.5 to 0.9 and a 1.7 percentage point increase in the Average Recall (AR) across the same thresholds.

MonoCAPE: Monocular 3D object detection with coordinate-aware position embeddings

3D monocular detection remains to be a focal point of research, particularly due to its capacity to deliver available precision under conditions of low cost and simplified configurations, making it especially valuable in fields like autonomous driving. Current 3D object detection methods often overlook the spatial information missing from images, which is critical to spatial perception, and optimize bounding box attributes separately, failing to meet the requirements of autonomous driving. We introduce MonoCAPE, a novel 3D detection framework addressing these issues by encoding spatial information and co-optimizing attributes through a Coordinate-Aware Position Encoding (CAPE) Generator and a Task Co-optimization Strategy (TCS). The CAPE Generator produces sparse positional embeddings, enabling spatial awareness with low computational cost, while the TCS utilizes Gaussian modeling to prevent suboptimal outputs. In this way, our framework comprehensively takes into account what existing approaches ignore. Extensive experiments on the KITTI dataset demonstrate MonoCAPE significantly improves AP3D and APBEV metrics compared to existing advanced methods.

AI-empowered Search Drone: Advanced AI-based Tiny Object Search Drone for Broad Areas

Abstract. Detecting the garbage distribution and position in broad terrains(beaches, grasslands, squares, etc) is extremely important for environmental protection and urban image. Manually screen these terrains is time-consuming and ineicient. Autonomous robot may find the wastes automatically, but they may hinder and even endanger the pedestrians. Unmanned aerial systems(UAS) are ideal tools for monitoring broad areas and detecting garbage. In this project we create an AI-empowered Search Drone for extensive garbage search in broad regions. The camera on our drone and the external image transmission module on the computer receive and process top-view image from the sky. The images from high altitude lead to the reduction in target size and object distortion. We innovately modified the network structure of the oicial YOLOv5 model by adding layers specifically for small object detection, enhancing its adaptability to small targets captured by drones.To address camera image distortion, we leveraged the OpenCV (cv2) library to obtain and apply a distortion correction matrix. Furthermore, we devised an algorithm that calculates the real-world location of targets based on their pixel positions, the drones height, and the ratio between pixel and real-world dimensions.To strengthen the models garbage search capabilities, we simulated various garbage search scenarios in open environments and collected image datasets using drones for specialized training. Finally, we utilized the PyQt library to develop a simplistic front-end interface for data analysis and comparison of the detection performance between the original and optimized models. Experimental results demonstrate that the optimized model outperforms the original model, effectively enhancing garbage search eiciency in extensive areas. This project represents a significant step forward in leveraging technology to combat environmental pollution.

Deep learning networks-based tomato disease and pest detection: a first review of research studies using real field datasets.

Recent advances in deep neural networks in terms of convolutional neural networks (CNNs) have enabled researchers to significantly improve the accuracy and speed of object recognition systems and their application to plant disease and pest detection and diagnosis. This paper presents the first comprehensive review and analysis of deep learning approaches for disease and pest detection in tomato plants, using self-collected field-based and benchmarking datasets extracted from real agricultural scenarios. The review shows that only a few studies available in the literature used data from real agricultural fields such as the PlantDoc dataset. The paper also reveals overoptimistic results of the huge number of studies in the literature that used the PlantVillage dataset collected under (controlled) laboratory conditions. This finding is consistent with the characteristics of the dataset, which consists of leaf images with a uniform background. The uniformity of the background images facilitates object detection and classification, resulting in higher performance-metric values for the models. However, such models are not very useful in agricultural practice, and it remains desirable to establish large datasets of plant diseases under real conditions. With some of the self-generated datasets from real agricultural fields reviewed in this paper, high performance values above 90% can be achieved by applying different (improved) CNN architectures such as Faster R-CNN and YOLO.

EMB-YOLO: A Lightweight Object Detection Algorithm for Isolation Switch State Detection

In power inspection, it is crucial to accurately and regularly monitor the status of isolation switches to ensure the stable operation of power systems. However, current methods for detecting the open and closed states of isolation switches based on image recognition still suffer from low accuracy and high edge deployment costs. In this paper, we propose a lightweight object detection model, EMB-YOLO, to address this challenge. Firstly, we propose an efficient mobile inverted bottleneck convolution (EMBC) module for the backbone network. This module is designed with a lightweight structure, aimed at reducing the computational complexity and parameter count, thereby optimizing the model’s computational efficiency. Furthermore, an ELA attention mechanism is used in the EMBC module to enhance the extraction of horizontal and vertical isolation switch features in complex environments. Finally, we proposed an efficient-RepGDFPN fusion network. This network integrates feature maps from different levels to detect isolation switches at multiple scales in monitoring scenarios. An isolation switch dataset was self-built to evaluate the performance of the proposed EMB-YOLO. The experimental results demonstrated that the proposed method achieved superior detection performance on our self-built dataset, with a mean average precision (mAP) of 87.2%, while maintaining a computational cost of only 6.5×109 FLOPs and a parameter size of just 2.8×106 bytes.

Estimation of Tree Diameter at Breast Height from Aerial Photographs Using a Mask R-CNN and Bayesian Regression

A probabilistic estimation model for forest biomass using unmanned aerial vehicle (UAV) photography was developed. We utilized a machine-learning-based object detection algorithm, a mask region-based convolutional neural network (Mask R-CNN), to detect trees in aerial photographs. Subsequently, Bayesian regression was used to calibrate the model based on an allometric model using the estimated crown diameter (CD) obtained from aerial photographs and analyzed the diameter at breast height (DBH) data acquired through terrestrial laser scanning. The F1 score of the Mask R-CNN for individual tree detection was 0.927. Moreover, CD estimation using the Mask R-CNN was acceptable (rRMSE = 10.17%). Accordingly, the probabilistic DBH estimation model was successfully calibrated using Bayesian regression. A predictive distribution accurately predicted the validation data, with 98.6% and 56.7% of the data being within the 95% and 50% prediction intervals, respectively. Furthermore, the estimated uncertainty of the probabilistic model was more practical and reliable compared to traditional ordinary least squares (OLS). Our model can be applied to estimate forest biomass at the individual tree level. Particularly, the probabilistic approach of this study provides a benefit for risk assessments. Additionally, since the workflow is not interfered by the tree canopy, it can effectively estimate forest biomass in dense canopy conditions.

Objective detection of wheeze at home by parents through a digital device: usage patterns and relationship with SABA administration.

Wheezing is an important indicator of exacerbated respiratory symptoms in early childhood and must be monitored to regulate pharmacological therapy. However, parents' subjective perception of wheezing in their children is not always precise. We investigated the objective identification of children's wheezing by parents using a digital wheeze detector (WheezeScanTM, OMRON Healthcare Co. Ltd), its longitudinal usage patterns, and its relationship with SABA administration. We conducted a secondary nested analysis of data from the intervention arm of a multi-center randomized controlled trial completed in 2021-2022 in Berlin (Germany), London (United Kingdom), and Istanbul (Turkey). Children aged 4 to 84 months with doctor's diagnosed wheezing (GINA step 1 or 2) were included. Using an electronic diary (Wheeze-MonitorTM, TPS), parents monitored and recorded for 120 days at home the presence or absence of their child's wheezing, detected both, with WheezeScanTM ("objective" wheezing), and subjective ("perceived" wheezing). Parents also recorded the child's symptoms, medication intake, and family quality of life. Questionnaires regarding symptom control, quality of life, and parental self-efficacy were answered at baseline and after 90 and 120 days. Eighty-one/87 families completed the intervention arm of the study. WheezeScanTM was on average used 0.7 (SD 0.6) times a day, with each patient reporting a positive, negative, or "error" outcome on average in 57%, 39%, and 5% of measurements, respectively. The use of WheezeScanTM declined slightly during the first 90 days of monitoring and steeply thereafter. Repeated usage of WheezeScanTM in the same day was more frequent after a "wheeze" (HR 1.5, 95% CI 1.37-1.65, p < 0.001) and an "error" (HR 2.01, 95% CI 1.70-2.38, p < 0.001) result, compared to a "no wheeze" outcome. The average per-patient daily agreement between "objective" and "perceived" wheezing/non-wheezing was 75% at the start of the monitoring period and only weakly persisted as time passed (Spearman's rho=0.09). The frequency of short-acting beta-2-agonists (SABA) administration was lower in days with closely interspaced consecutive device uses during which the patient's status was perceived as "never wheeze" (32/455, 7%) than in those perceived as "persistent wheeze" (53/119, 44%; OR 36.6, 95% CI [14.3, 94.1]). Daily use of a digital WheezeScanTM at home allows parents to detect their child's unperceived wheezing and discloses to caregivers the longitudinal patterns of a child's wheezing disorder. Digital monitoring of wheezing also highlights poor adherence to guidelines in SABA administration for wheezing children, with under-treatment being much more frequent than over-treatment. This pioneering study opens new perspectives for further investigation of digital wheeze detectors in the early diagnosis and proper self-management of wheezing disorders in childhood.

Evaluating Convolutional Neural Network Architecture for Historical Topographic Hardcopy Maps Analysis: A Study on Training and Validation Accuracy Variation

Convolutional Neural Networks (CNN) are widely used for image analysis tasks, including object detection, segmentation, and recognition. Given the advanced capability, this study evaluates the effectiveness and performance of CNN architecture for analysing Historical Topographic Hardcopy Maps (HTHM) by assessing variations in training and validation accuracy. The lack of research specifically dedicated to CNN’s application in analysing topographic hardcopy maps presents an opportunity to explore and address the unique challenges associated with this domain. While existing studies have predominantly focused on satellite imagery, this study aims to uncover valuable insights, patterns, and characteristics inherent to HTHM through customised CNN approaches. This study utilises a standard CNN architecture and tests the model’s performance with different epoch settings (20, 40, and 60) using varying dataset sizes (288, 636, 1144, and 1716 images). The results indicate that the optimal operation point for training and validation accuracy is achieved at epoch 40. Beyond epoch 40, the widening gap between training and validation accuracy suggests overfitting. Hence, adding more epochs does not significantly improve accuracy beyond the optimum phase. The experiment also shows that the CNN model obtains a training accuracy of 98%, validation accuracy of 67%, and F1-score overall performance of 77%. The analysis demonstrates that the CNN model performs reasonably well in classifying instances from the HTHM dataset. These findings contribute to a better understanding of the strengths and limitations of the model, providing valuable insights for future research and refinement of classification approaches in the context of topographic hardcopy map analysis.

YOLO-DroneMS: Multi-Scale Object Detection Network for Unmanned Aerial Vehicle (UAV) Images

In recent years, research on Unmanned Aerial Vehicles (UAVs) has developed rapidly. Compared to traditional remote-sensing images, UAV images exhibit complex backgrounds, high resolution, and large differences in object scales. Therefore, UAV object detection is an essential yet challenging task. This paper proposes a multi-scale object detection network, namely YOLO-DroneMS (You Only Look Once for Drone Multi-Scale Object), for UAV images. Targeting the pivotal connection between the backbone and neck, the Large Separable Kernel Attention (LSKA) mechanism is adopted with the Spatial Pyramid Pooling Factor (SPPF), where weighted processing of multi-scale feature maps is performed to focus more on features. And Attentional Scale Sequence Fusion DySample (ASF-DySample) is introduced to perform attention scale sequence fusion and dynamic upsampling to conserve resources. Then, the faster cross-stage partial network bottleneck with two convolutions (named C2f) in the backbone is optimized using the Inverted Residual Mobile Block and Dilated Reparam Block (iRMB-DRB), which balances the advantages of dynamic global modeling and static local information fusion. This optimization effectively increases the model’s receptive field, enhancing its capability for downstream tasks. By replacing the original CIoU with WIoUv3, the model prioritizes anchoring boxes of superior quality, dynamically adjusting weights to enhance detection performance for small objects. Experimental findings on the VisDrone2019 dataset demonstrate that at an Intersection over Union (IoU) of 0.5, YOLO-DroneMS achieves a 3.6% increase in mAP@50 compared to the YOLOv8n model. Moreover, YOLO-DroneMS exhibits improved detection speed, increasing the number of frames per second (FPS) from 78.7 to 83.3. The enhanced model supports diverse target scales and achieves high recognition rates, making it well-suited for drone-based object detection tasks, particularly in scenarios involving multiple object clusters.

SOD‐diffusion: Salient Object Detection via Diffusion‐Based Image Generators

AbstractSalient Object Detection (SOD) is a challenging task that aims to precisely identify and segment the salient objects. However, existing SOD methods still face challenges in making explicit predictions near the edges and often lack end‐to‐end training capabilities. To alleviate these problems, we propose SOD‐diffusion, a novel framework that formulates salient object detection as a denoising diffusion process from noisy masks to object masks. Specifically, object masks diffuse from ground‐truth masks to random distribution in latent space, and the model learns to reverse this noising process to reconstruct object masks. To enhance the denoising learning process, we design an attention feature interaction module (AFIM) and a specific fine‐tuning protocol to integrate conditional semantic features from the input image with diffusion noise embedding. Extensive experiments on five widely used SOD benchmark datasets demonstrate that our proposed SOD‐diffusion achieves favorable performance compared to previous well‐established methods. Furthermore, leveraging the outstanding generalization capability of SOD‐diffusion, we applied it to publicly available images, generating high‐quality masks that serve as an additional SOD benchmark testset.

Development and performance evaluation of an indoor thermal environment control algorithm incorporating MET estimation model with object detection

As people increasingly spend time indoors, the significance of the indoor environment in influencing occupant quality of life is becoming more pronounced. Traditionally, indoor environment control primarily relied on fixed temperature settings, which failed to accommodate the diverse circumstances of occupants. This approach limited the creation of a comfortable indoor environment and the enhancement of energy efficiency. Consequently, there is growing interest in occupant-centric control (OCC), which integrates metabolic rate (MET) information, which is a critical factor in determining the thermal sensation of occupants. Previously, a method was developed to estimate MET by classifying occupant poses and detecting the objects they interact with from indoor images. This study aims to develop and experimentally validate an indoor thermal environment control algorithm (ITEC-algorithm) using the MET estimation model and assess its effectiveness and applicability in real building environments.The performance evaluation revealed that the ITEC-algorithm significantly enhanced the comfort ratios, achieving improvements of up to 59% compared to the fixed temperature control and 28% compared to the control methods that only used the pose classification model for MET estimation. The energy consumption varied depending on the activity and control method, with a reduction of up to 88% compared to fixed temperature control. These results indicate that thermal comfort can be enhanced while minimizing unnecessary energy consumption by incorporating the MET of the occupants. Consequently, it has been confirmed that the ITEC-algorithm effectively improves thermal comfort by managing the MET of various occupants.

Semi-Supervised Online Continual Learning for 3D Object Detection in Mobile Robotics

Continual learning addresses the challenge of acquiring and retaining knowledge over time across multiple tasks and environments. Previous research primarily focuses on offline settings where models learn through increasing tasks from samples paired with ground truth annotations. In this work, we focus on an unsolved, challenging, yet practical scenario, specifically, the semi-supervised online continual learning in autonomous driving and mobile robotics. In our settings, models are tasked with learning new distributions from streaming unlabeled samples and performing 3D object detection as soon as the LiDAR point cloud arrives. Additionally, we conducted experiments on both the KITTI dataset, our newly built IUSL dataset and Canadian Adverse Driving Conditions (CADC) Dataset. The results indicate that our method achieves a balance between rapid adaptation and knowledge retention, showcasing its effectiveness in the dynamic and complex environment of autonomous driving and mobile robotics. The developed ROS packages and IUSL dataset will be publicly available at: https://github.com/npu-ius-lab/OCL3D.

Retraction Note: Military object detection in defense using multi-level capsule networks

Retraction Note: Military object detection in defense using multi-level capsule networks

LES-YOLO: efficient object detection algorithm used on UAV for traffic monitoring

Abstract The use of UAVs for traffic monitoring greatly facilitates people’s lives. Classical object detection algorithms struggle to balance high speed and accuracy when processing UAV images on edge devices. To solve the problem, the paper introduces an efficient and slim YOLO with low computational overhead, named LES-YOLO. In order to enrich the feature representation of small and medium objects in UAV images, a redesigned backbone is introduced. And C2f combined with Coordinate Attention is used to focus on key features. In order to enrich cross-scale information and reduce feature loss during network transmission, a novel structure called EMS-PAN (Enhanced Multi-Scale PAN) is designed. At the same time, to alleviate the problem of class imbalance, Focal EIoU is used to optimize network loss calculation instead of CIoU. To minimize redundancy and ensure a slim architecture, the P5 layer has been eliminated from the model. And verification experiments show that LES-YOLO without P5 is more efficient and slimmer. LES-YOLO is trained and tested on the VisDrone2019 dataset. Compared with YOLOv8n-p2, mAP@0.5 and Recall has increased by 7.4% and 7%. The number of parameters is reduced by over 50%, from 2.9 M to 1.4 M, but there is a certain degree of increase in FLOPS, reaching 18.8 GFLOPS. However, the overall computational overhead is still small enough. Moreover, compared with YOLOv8s-p2, both the number of parameters and FLOPS are significantly reduced , while the performance is similar . As for real-time, LES-YOLO reaches 138 fps on GPU and a maximum of 78 fps on edge devices of UAV.

Feature-aware and iterative refinement network for camouflaged object detection

Feature-aware and iterative refinement network for camouflaged object detection

“Idol talks!” AI-driven image to text to speech: illustrated by an application to images of deities

This work aims to provide an innovative solution to enhance the accessibility of images by an innovative image to text to speech system. It is applied to Hindu and Christian divine images. The method is applicable, among others, to enhance cultural understanding of these images by the visually impaired. The proposed system utilizes advanced object detection techniques like YOLO V5 and caption generation techniques like ensemble models. The system accurately identifies significant objects in images of Deities. These objects are then translated into descriptive and culturally relevant text through a Google text-to-speech synthesis module. The incorporation of text generation techniques from images introduces a new perspective to the proposed work. The aim is to provide a more comprehensive understanding of the visual content and allow visually impaired individuals to connect with the spiritual elements of deities through the immersive experience of auditory perception through a multimodal approach to make them feel inclusive in the community. This work is also applicable to preserve Cultural Heritage, Tourism and integrating with Virtual Reality (VR) and Augmented Reality (AR). Images of the artistic cultural legacy are hardly available in annotated databases, particularly those featuring idols. So we gathered, transcribed, and created a new database of Religious Idols in order to satisfy this requirement. In this paper, we experimented how to handle an issue of religious idol recognition using deep neural networks. In order to achieve this outcome, the network is first pre-trained on various deep learning models, and the best one which outperforms others is chosen. The proposed model achieves an accuracy of 96.75% for idol detection, and an approximate 97.06% accuracy for text generation according to the BLEU score.

Multiple moving object classification and tracking using DenCNN classifier

A Multiple moving object detection, tracking, and counting algorithm is mainly designed exclusively suitable for congested areas. The counting system can alleviate the betrayal performance in the crowded areas. Most of the existing methods developed for tracking and counting face serious challenges in detection due to high densities of the target. This condition urged the researchers to update the existing systems. The present methodology was designed to address such issues. In the present methodology, the contrast was initially enhanced between the objects and their backgrounds using a Double Plateau Histogram Equalization (DPHE). Then, the motion was estimated for the contrast-enhanced image to identify the moment of the object using the modified Adaptive Distance Covariance Rood Pattern Search (ADCRPS) algorithm. After that, the morphological operation was deployed to sharpen the images by removing all the unwanted things. Then, the features were extracted and important features were selected using the modified Chaotic Tent Shuffled Shepherd Optimization (CTSSO) Algorithm. With the selected features object, detection was done using the proposed Scaled Non-Monotonic Cauchy Dense Convolutional Neural Network (SNMC-DenCNN). The detected object was then tracked with the aid of Channel and Spatial Reliability Tracker (CSRT). Finally, the objects were counted by intersection over union (IOU) by explicitly computing the association between detected and tracked objects. Also, the experimental results showed the effectiveness and efficiency of the proposed system with enhanced accuracy.