Computer Vision-Based Behavior Analysis for Workplace Efficiency: Bakery Environment Application

Computer vision (CV) and human-computer interaction (HCI) are essential in many technological fields. Researchers in CV are particularly interested in real-time object detection techniques, which have a wide range of applications, including inspection systems. In this study, we design and implement real-time object detection and recognition systems using the single-shoot detector (SSD) algorithm and deep learning techniques with pre-trained models. The system can detect static and moving objects in real-time and recognize the object's class. The primary goals of this research were to investigate and develop a real-time object detection system that employs deep learning and neural systems for real-time object detection and recognition. In addition, we evaluated the free available, pre-trained models with the SSD algorithm on various types of datasets to determine which models have high accuracy and speed when detecting an object. Moreover, the system is required to be operational on reasonable equipment. We tried and evaluated several deep learning structures and techniques during the coding procedure and developed and proposed a highly accurate and efficient object detection system. This system utilizes freely available datasets such as MS Common Objects in Context (COCO), PASCAL VOC, and Kitti. We evaluated our system's accuracy using various metrics such as precision and recall. The proposed system achieved a high accuracy of 97% while detecting and recognizing real-time objects.

Recently, drones have been applied and used in various tasks in many fields. Due to the development of camera technology and hardware, it is possible to detect objects using deep learning technology in real time. Most of the object sizes of videos and images taken by drones are very small objects. The dataset that collects these image data is the VisDrone dataset. As deep learning technology develops a lot, object detection accuracy is getting higher and higher. However, it is still hard to perform object detection in real-time and show high object detection accuracy. In this paper, we combine YOLOv5 and CBAM to increase object detection accuracy by focusing more on the features required for object detection. The model is trained using the VisDrone dataset, and the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{mAP}$</tex> value is measured at 22. <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$56\text{mAP}$</tex> ,which is 2. <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$45\text{mAP}$</tex> higher than the original YOLOv5.

To further improve the speed and accuracy of object detection, especially small targets and occluded objects, a novel and efficient detector named YOLO-ACN is presented. The detector model is inspired by the high detection accuracy and speed of YOLOv3, and it is improved by the addition of an attention mechanism, a CIoU (complete intersection over union) loss function, Soft-NMS (non-maximum suppression), and depthwise separable convolution. First, the attention mechanism is introduced in the channel and spatial dimensions in each residual block to focus on small targets. Second, CIoU loss is adopted to achieve accurate bounding box (BBox) regression. Besides, to filter out a more accurate BBox and avoid deleting occluded objects in dense images, the CIoU is applied in the Soft-NMS, and the Gaussian model in the Soft-NMS is employed to suppress the surrounding BBox. Third, to significantly reduce the parameters and improve the detection speed, standard convolution is replaced by depthwise separable convolution, and hard-swish activation function is utilized in deeper layers. On the MS COCO dataset and infrared pedestrian dataset KAIST, the quantitative experimental results show that compared with other state-of-the-art models, the proposed YOLO-ACN has high accuracy and speed in detecting small targets and occluded objects. YOLO-ACN reaches a mAP50 (mean average precision) of 53.8% and an APs (average precision for small objects) of 18.2% at a real-time speed of 22 ms on the MS COCO dataset, and the mAP for a single class on the KAIST dataset even reaches over 80% on an NVIDIA Tesla K40.

Abstract: Satellite image analysis plays a crucial role in fields such as environmental monitoring, urban planning, agriculture, and disaster management. This project presents a machine learning-based methodology for the automated segmentation and classification of objects in satellite images. The proposed system employs a two-step algorithm: (1) K-Means clustering for unsupervised segmentation of satellite images into meaningful clusters, and (2) Support Vector Machine (SVM) for supervised classification of the segmented regions into predefined categories such as land, water, and vegetation. A Flask-based web application is developed to provide a userfriendly interface for uploading satellite images and visualizing classified results. The backend is supported by a structured SQL database to manage image metadata and classification outputs efficiently. The system is designed to handle large datasets, ensuring robustness and scalability. Experimental results on benchmark datasets demonstrate high accuracy in object detection and classification, highlighting the system's potential to support real-world decision-making in environmental and infrastructural domains.

Conventional street cleaning methods include street sweepers going to various spots in the city and manually verifying if the street needs cleaning and taking action if required. However, this method is not optimized and demands a huge investment in terms of time and money. This paper introduces an automated framework which addresses street cleaning problem in a better way by making use of modern equipment with cameras and computational techniques to analyze, find and efficiently schedule clean-up crews for the areas requiring more attention. Deep learning-based neural network techniques can be used to achieve better accuracy and performance for object detection and classification than conventional machine learning algorithms for large volume of images. The proposed framework for street cleaning leverages the deep learning algorithm pipeline to analyze the street photographs and determines if the streets are dirty by detecting litter objects. The pipeline further determines the the degree to which the streets are littered by classifying the litter objects detected in earlier stages. The framework also provides information on the cleanliness status of the streets on a dashboard updated in real-time. Such framework can prove effective in reducing resource consumption and overall operational cost involved in street cleaning.

The COVID-19 pandemic has had a significant impact on human migration worldwide, affecting transportation patterns in cities. Many cities have issued "stay-at-home" orders during the outbreak, causing commuters to change their usual modes of transportation. For example, some transit/bus passengers have switched to driving or car-sharing. As a result, urban traffic congestion patterns have changed dramatically, and understanding these changes is crucial for effective emergency traffic management and control efforts. While previous studies have focused on natural disasters or major accidents, only a few have examined pandemic-related traffic congestion patterns. This paper uses correlations and machine learning techniques to analyze the relationship between COVID-19 and transportation. The authors simulated traffic models for five different networks and proposed a Traffic Prediction Technique (TPT), which includes an Impact Calculation Methodology that uses Pearson's Correlation Coefficient and Linear Regression, as well as a Traffic Prediction Module (TPM). The paper's main contribution is the introduction of the TPM, which uses Convolutional Neural Network to predict the impact of COVID-19 on transportation. The results indicate a strong correlation between the spread of COVID-19 and transportation patterns, and the CNN has a high accuracy rate in predicting these impacts.

Traditional cloud computing faces issues like high bandwidth pressure, service delay, and energy consumption, while simple edge computing struggles with complex tasks due to limited resources. Thus, a collaborative edge cloud data processing and adaptive optimization method for intelligent perception systems is proposed. A three-layer model (terminal, edge cluster, cloud center) formalizes tasks, communication, and computing resources for dynamic optimization. A collaborative data processing flow is designed, with lightweight terminal preprocessing based on priority and an intelligent edge caching mechanism to reduce redundant data transmission. Task offloading and resource allocation are framed as reinforcement learning tasks, using the PPO algorithm for intelligent decision-making. Experiments show average processing latencies of 150.2-260.1 ms under varying loads, achieving high energy efficiency (98.5 FLOPs/J, Floating-point Operations per Joule) and object detection accuracy (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$97.2 \% \text{mAP}$</tex>, mean Average Precision). The method outperforms others in latency and balances energy efficiency and accuracy, efficiently utilizing resources amid dynamic tasks and uncertainties.

Using deep learning techniques on radiological lung images for detecting COVID-19 is a promising technique in shortening the diagnosis time. In this study, we propose a hybrid deep learning model, detecting the COVID-19 and Pneumonia virus using Chest X-ray images. The proposed model, named SpiCoNET, first runs multiple well-known deep learning models combined with Spiking Neural Network (SNN) in order to identify the models with higher accuracy rates. Then, SpiCoNET combines the features of the two models with the highest accuracy rates among the well-known models and hands the combined features over to a different SNN layer as an input. Finally, the features are classified by using the SEFRON learning algorithm. The proposed hybrid deep learning model takes advantage of the features of the well-known models combined with SNN providing the highest accuracy rate. Moreover, the proposed model makes use of the SEFRON learning algorithm to provide better classification. The proposed model provides an accuracy rate of 97.09% for the classification of images of the COVID-19, Pneumonia and Normal, which outperforms AlexNet (91.27%) and DenseNet201 (90.40%). The results reveal that deep learning based systems for the identification of COVID-19 and Pneumonia can help healthcare professionals control the COVID-19 pandemic in an effective manner.

Role of deep learning in early detection of COVID-19: Scoping review

SummaryAs various algorithms and models spring up, object detection‐based deep learning for image analysis has become an established technology in the field of computer vision. Furthermore, object detection for high‐resolution videos derived from ubiquitous surveillance cameras draws many researchers' attention due to its practical significance and the challenge on detecting accuracy and speed. Existing object detection methods mainly resize and compress each frame in the video and then apply the detection algorithm, but with the method, the detection for small objects in dense scenes is far from satisfactory in terms of accuracy because small objects are reduced to pixel‐level sizes in compressed frame images. In order to fix this issue, we propose AROD, a parallel detection framework based on adaptive image cropping. Unlike the traditional first‐compression‐and‐then‐detection methods, AROD adopts adaptive image cropping for distributed parallel detection. In this way, AROD significantly improves the accuracy of small object detection in dense scenes with acceptable overhead. In our experimental evaluation, YOLOv2‐tiny model equipped with AROD reaches 29.29 FPS along with high detection accuracy. Experimental results verify that AROD ensures the high throughput of real‐time video analytics while maintaining high detection accuracy.

Object detection for mobile devices is meaningful especially in the field of IoT. Limited by computing power and network transmission, it's challenging to get high accuracy in mobile object detection. To solve this question, this article designs a system that enables high accuracy object detection running at 30fps for 720p videos. The system employs the object tracking technique, uses the caching technique, decouples the rendering pipeline from the offloading pipeline, and uses dynamic RoI encoding technique to get high detection accuracy. The result of the experiment shows that it can get 88% detection success rate. And it can also increase the detection accuracy by 17.7% and decrease the bandwidth by 52.6%.

Construction sites are inherently hazardous, with risks such as falls, fire incidents, and non-compliance with personal protective equipment (PPE) leading to significant injuries and fatalities. Traditional safety monitoring methods, reliant on manual inspections and basic sensor systems, suffer from inefficiencies and human error. This paper proposes a real-time construction site accident detection framework utilizing computer vision and deep learning techniques. The system integrates three key modules: helmet detection using YOLOv8, fall detection based on YOLOv8 with aspect ratio analysis, and fire/smoke detection using OpenCV-based color and motion analysis. Detected violations or hazards trigger on-screen alerts and audible siren alarms, with events logged for record-keeping. The proposed approach eliminates the need for manual supervision, offering a scalable and cost-effective solution suitable for diverse construction environments. Experimental evaluations demonstrate the capability of the framework to operate under varying lighting and environmental conditions with reliable accuracy. The software-based system achieves real-time performance and provides a foundation for future integration with IoT devices, enhancing construction site safety through automated monitoring and intelligent decision-making.

This study investigates a class-splitting strategy for improving object detection under limited training data using YOLOv11n with transfer learning and data augmentation for agricultural images containing leaves and peppers. The proposed approach evaluates leaf-only, pepper-only, and combined-class configurations using mAP@0.5, mAP@0.5:0.95, precision, recall, and F1-score to examine how class splitting affects detection performance. On the small validation set used in this study, single-class training improved performance relative to the combined-class baseline, but the results should be interpreted as preliminary because the validation set contains only two samples.

Recently, generative models, such as ChatGPT, have gained considerable attention because of their capacity to generate text almost identical to that produced by humans. However, ChatGPT raises several concerns, particularly regarding the integrity of academic work, the protection of personal information and security, the reliance on artificial intelligence (AI), the evaluation of learning, and the precision of information. Distinguishing between writing generated by machines and text that humans wrote is one of the most critical issues at present. The purpose of this literature review is to provide a comprehensive, up-to-date analysis of the most recent methods for identifying text that ChatGPT created. It examines more than 60 academic papers, especially research articles published after the model’s release in 2022, and analyzes state-of-the-art machine learning, deep learning, and hybrid approaches for detecting AI-generated text. The review categorizes detection methods into statistical models, transformer-based architectures, perplexity-based techniques, and human-assisted evaluation. The findings indicate that deep learning models, particularly the Robustly Optimized BERT Pretraining Approach (RoBERTa) and Cross-lingual Language Model with RoBERTa Architecture, have high detection accuracy (up to 99%), whereas traditional statistical methods exhibit limitations in distinguishing complex AI-generated content. This work recommends the use of machine and deep learning techniques and human reviewers in ongoing efforts to distinguish between AI-generated and human-written text. However, given the increasing sophistication and complexity of models, such as ChatGPT, detection techniques have to be continuously improved and innovated to ensure reliability and maintain the integrity of content across various sectors.

Banking is one of the fine service sectors for providing various financial services to its customers especially in the agriculture and allied sectors. Indian banking industry is rendering their services according to the directions of Reserve Bank of India. As banking industry is service providing industry and customers’ satisfaction is vital in its business. It is observed that mostly customers are satisfied with the banking services but some low satisfaction regarding the responsiveness and individual attention of customers by the banks employees. Customers are dissatisfied because of nature and behavior of the banking employees. There is discrimination in behavior of employees according to the profession of the customers. This leads to provide special training to change attitude of the employees towards customers. The customers are encountering problems might be because of insufficient staff or staff should be behaviorally equipped with to satisfy their needs to enhance satisfaction level.