149 Developing and validating artificial intelligence models to predict cattle behavior, movement, and emotion

Object detection is a fundamental problem in computer vision, with applications spanning self-driving cars, surveillance systems, medical imaging, robotics, and smart cities. Among the myriad of algorithms developed for this task, the You Only Look Once (YOLO) family stands out for its ability to perform real-time and accurate object detection. This article provides a comprehensive analysis of the YOLO algorithm series, from YOLOv1 to YOLOv8, evaluating them across key performance metrics, including precision, recall, mean Average Precision (mAP), frames per second (FPS), and overall effectiveness. Unlike traditional two-stage detectors such as R-CNN, YOLO formulates object detection as a single regression problem: a single pass over the image simultaneously predicts bounding boxes and class probabilities. This end-to-end design enables YOLO to achieve high speed while maintaining a competitive accuracy-efficiency trade-off. We examine architectural innovations across YOLO versions, including batch normalization, anchor boxes, residual blocks, feature pyramid networks, and attention mechanisms, and discuss their impact on performance. Lightweight models (e.g., YOLOv5-Nano, YOLOv8-Small) are explored with a focus on their suitability for mobile and embedded systems, highlighting YOLO’s adaptability to resource-constrained environments. Challenges such as small object detection, occlusion, and domain-specific tuning are also addressed. This article serves as a practical guide for researchers, developers, and practitioners aiming to leverage YOLO for real-world object detection tasks.

Object detection has become the cornerstone of various real-world applications, ranging from autonomous driving where it helps detect road objects to medical imaging for early disease prediction and gesture recognition systems. Due to their ability to operate continuously and cost-effectively, computer vision models are increasingly being employed in surveillance and monitoring tasks. Unlike humans, who can intuitively recognize and understand objects within images, machines require advanced algorithms to mimic such capabilities. A key challenge in computer vision lies in recognizing and tracking objects in real-time with accuracy and reliability. Human visual systems perform these tasks naturally and swiftly, enabling complex activities like driving. For computers to match such performance, efficient object detection models along with additional hardware such as sensors are essential. This study presents a comparative analysis of two popular deep learning-based object detection algorithms You Only Look Once (YOLO) and Faster Region-based Convolution Neural Network (Faster RCNN). Both models are evaluated based on their detection accuracy, speed, and performance in real-time scenarios. The paper aims to highlight the strengths and limitations of each model, offering insights into their suitability for different applications. The findings suggest that while both algorithms have their merits, the choice between them depends on the specific requirements of a task, such as the trade-off between detection speed and precision. Comparative study shows that Faster RCNN outperforms its accuracy compared with YOLO.

Vision-based automatic detection and tracking of on-road vehicles is a demanding research area in intelligent transportation systems (ITS). Several investigation reports are available in the literature in this research field using various machine learning (ML) techniques. This article proposes a method for on-road vehicle detection and tracking in varying weather conditions by modifying the structure of you only look once (YOLO), a recently developed deep learning model in computer vision for detecting objects. ML-based systems repurpose classifiers to detect the vehicles, whereas in YOLO model, a single convolutional neural network (CNN) predicts the detecting bounding boxes and the class probabilities for those boxes together in one evaluation from the full images. As the entire detection is performed using a single network, the detection process can be viewed as an end-to-end system. In this work, 16 convolutional layers of YOLO have been used along with two fully connected layers at the end. The vehicles have been detected in varying weather conditions. Two different public data sets, namely CDNet 2014 and LISA 2010 data sets have been used to evaluate the system performance, and it outperforms state-of-the-art results.

Object detection from UAV thermal infrared images and videos using YOLO models

The natural phenomenon of harmful algae bloom (HAB) has a bad impact on the quality of pure and freshwater. It increases the risk to human health, water bodies and overall aquatic ecosystem. It is necessary to continuously monitor and perform proper action against HAB. The inspection of algae blooms by using conventional methods, like algae detection under microscopes, is a difficult, expensive, and time-consuming task, however, computer vision-based deep learning models play a vital role in identifying and detecting harmful algae growth in aquatic ecosystems and water reservoirs. Many studies have been conducted to address harmful algae growth by using a CNN based model, however, the YOLO model is considered more accurate in identifying the algae. This advanced deep learning method is extensively used to detect algae and classify them according to their corresponding category. In this study, we used various versions of the convolution neural network (CNN) based on the You Only Look Once (YOLO) model. Recently YOLOv5 has been getting more attention due to its performance in real-time object detection. We performed a series of experiments on our custom microscopic images dataset by using YOLOv3, YOLOv4, and YOLOv5 to detect and classify the harmful algae bloom (HAB) of four classes. We used pre-processing techniques to enhance the quantity of data. The mean average precision (mAP) of YOLOv3, YOLOv4, and YOLO v5 is 75.3%, 83.0%, and 91.0% respectively. For the monitoring of algae bloom in freshwater, computer-aided based systems are very helpful and effective. To the best of our knowledge, this work is pioneering in the AI community for applying the YOLO models to detect algae and classify from microscopic images.

Automatic target recognition (ATR) is a challenging task for several computer vision applications. It requires efficient, accurate, and robust methods for target detection and target identification. Deep learning has shown great success in many computer vision applications involving color RGB images. However, the performance of these networks in ATR with infrared sensor data needs further investigation. In this paper, we propose a multistage automatic target detection and recognition (ATDR) system that performs both target detection and target classification on infrared (IR) imagery using deep learning. Our system processes large IR image frames where targets take <1 % of the total number of pixels. First, we train a state-of-the-art object detector you only look once (YOLO) to localize all potential targets in the input image frame. Then, we train a convolutional neural network (CNN) to identify these detections as targets or false alarms. In this second phase, we adapt and analyze the performance of three CNN architectures: a compact and fully connected CNN, VGG16 with batch normalization, and a wide residual neural network (WRN). We also explore the use of a loss function that optimizes directly the area under the receiver operating characteristic (ROC) curve (AUC), and adapt it to our ATR application. To enhance the robustness of the proposed ATR to perturbation and variations introduced during the detection stage, we train our CNN classifiers on automatically detected targets using YOLO, in addition to ground truth bounding boxes and apply selected data augmentation techniques. To simulate real testing environments, where the spatial location of the targets within the image frame is unknown, only YOLO-detected boxes are used during validation. We evaluate our ATDR on a real benchmark dataset that includes different vehicles captured at different resolutions. Our experiments have shown that YOLO can detect most of the targets at the expense of generating a high number of false alarms. We show that the VGG-16 network with batch normalization, which is the best performing model, can correctly identify the classes of the targets, as well as classify the majority of YOLO’s false detections into an additional nontarget class. We also show that the proposed training modification to optimize an AUC-based loss function for ATR proved to be advantageous mainly in identifying difficult targets.

Nursery plays an important role on starting chili cultivation, determining the crop health, fertility from disease, and growth performance. Early-stage germination detection is necessary to minimize nursery failure and improve plant health, but manual detection is challenging for large scale nursery in the greenhouse. The aim of this research was to develop an automatic detection model integrated with a You Only Look Once (YOLO) based deep learning algorithm using RGB camera to monitor the chili germination stages. Method to detect germination was YOLO with several steps, included: (1) early stages chili germination images acquisition, (2) datasets preparations, (3) dataset annotation and labeling, (4) model development using deep learning YOLO algorithms, and (5) model testing and validation. The training of 11,423 images was conducted utilizing the YOLOv5 and YOLOv8 algorithms, which categorized into, three classes (germinated, not germinated, and cotyledon appearance). The model was evaluated using mean Average Precision (mAP), precision, accuracy, and recall with the respective values of 0.697, 73%, 75%, and 73% for YOLOv8, and 0.664, 70%, 73%, and 70% for YOLOv5. Both model achieved high accuracy, but YOLOv8 was better to detect and classify chili seedling growth stages than YOLOv5. This study also demonstrated that model can be implemented in real applications integrated with automatic monitoring system included in the model. Keywords: Chili seedling, Deep learning, Detection system, Germination.

Introduction. Research focused on listening and understanding public attitudes towards farm animal welfare is proliferating globally. Objective. To determine how consumers in Colombia perceive the welfare of farm animals and socio-demographic factors associated with such perceptions. Materials and methods. A descriptive and analytical epidemiological study was done through the implementation of a cross-sectional national online survey, conducted from September to October-2021, to obtain information on the knowledge and attitudes of consumers (≥18 years) in Colombia regarding farm animal welfare, as well as socio-demographic characteristics (sex, age, education, occupation, location, growing-up environment, level of contact with livestock farms, and diet). Logistic and multinomial logistic regression models were used to evaluate associations of demographic factors with the level of importance given to animal welfare (AW), perceptions about needs, behavior, and sentience in farm animals, and perceptions about the state and promotion of AW in Colombia. Results. Responses from 798 participants were included in the analysis, 85.57 % saw AW as a concept that refers to how to treat animals and improve their quality of life. The level of importance given to AW was 9.78±0.85 (scale from 0 to 10) and was associated with words such as health, food, caring, respect, and comfort. Sex, age, education, level of contact with a farm, and growing-up in rural areas significantly influenced the opinions regarding the concept of AW and the promotion of farm animal welfare in Colombia. Conclusion. The participants showed concern about farm animal welfare. The differences found associated with the different socio-demographic factors can be used as a basis for formulating education and empowerment strategies that help modulate changes in the way animals are seen, and what welfare means and what it implies.

The coronavirus disease (COVID-19) is primarily disseminated through physical contact. As a precaution, it is recommended that indoor spaces have a limited number of people and at least one meter apart. This study proposes a real-time method for monitoring physical distancing compliance in indoor spaces using computer vision and deep learning techniques. The proposed method utilizes YOLO (You Only Look Once), a popular convolutional neural network-based object detection model, pre-trained on the Microsoft COCO (Common Objects in Context) dataset to detect persons and estimate their physical distance in real time. The effectiveness of the proposed method was assessed using metrics including accuracy rate, frame per second (FPS), and mean average precision (mAP). The results show that the YOLO v3 model had the most remarkable accuracy (87.07%) and mAP (89.91%). On the other hand, the highest fps rate of up to 18.71 was achieved by the YOLO v5s model. The results demonstrate the potential of the proposed method for effectively monitoring physical distancing compliance in indoor spaces, providing valuable insights for future use in other public health scenarios.

Counterfeit and substandard pharmaceuticals represent a critical global health crisis, with the World Health Organisation (WHO) reporting that falsified medicines comprise 10% of the global pharmaceutical trade, constituting one of the fastest-growing grey economies worldwide. This illicit market affects all regions, including high-income countries, causing devastating health and economic consequences that contribute to increased mortality and morbidity rates. The problem is particularly severe in developing nations with inadequate regulatory systems. Mobile health (mHealth) technologies have emerged as promising solutions for enhancing pharmaceutical supply chain integrity. However, comprehensive frameworks that integrate multiple authentication mechanisms remain limited in addressing the growing counterfeit drug crisis. The primary objective of this study is to design and implement a novel mobile health framework that integrates innovative packaging technology and computer vision to enhance pharmaceutical integrity and patient safety. This innovative approach enables consumers to authenticate genuine drugs and detect counterfeit/spurious products through advanced Quick Response (QR) code verification systems and artificial intelligence- powered tablet recognition capabilities. The proposed system utilises individual strip QR codes that enable real-time scanning at the point of sale to verify drug authenticity, directly addressing critical gaps identified in current pharmaceutical authentication methods. To overcome the practical challenge of expiration date loss when medication strips are cut at pharmacies, we developed a computer vision-based Artificial Intelligence (AI) model that automatically recognises the number of tablets remaining in a strip and correlates this information with the unique Identifier (ID). This approach leverages recent advances in computer vision for pharmaceutical applications and automated packaging inspection technologies. Each medication strip is assigned to an individual customer at the time of purchase, with pharmacists recording detailed customer information to ensure comprehensive tracking and accountability throughout the pharmaceutical supply chain. The integrated mobile application creates a robust anti-counterfeiting ecosystem by combining secure QR code authentication with intelligent visual recognition capabilities. The computer vision model provides accurate tablet counting and strip identification, maintaining continuity of medication tracking even when packaging is modified during dispensing processes, thus significantly enhancing supply chain transparency. This dual-authentication approach builds consumer confidence in pharmaceutical authenticity while directly addressing critical vulnerabilities identified in current regulatory frameworks. This comprehensive mobile health solution provides a scalable, evidence-based approach to pharmaceutical authentication that can be readily implemented across diverse healthcare systems globally, offering substantial potential for reducing the circulation of falsified medicines and improving patient safety outcomes in the ongoing battle against the pandemic of counterfeit pharmaceuticals. To overcome the practical challenge of expiration date loss when medication strips are cut at pharmacies, we developed a computer vision-based Artificial Intelligence (AI) model that automatically recognises the number of tablets remaining in a strip and correlates this information with the unique Identifier (ID). This approach leverages recent advances in computer vision for pharmaceutical applications and automated packaging inspection technologies. Each medication strip is assigned to an individual customer at the time of purchase, with pharmacists recording detailed customer information to ensure comprehensive tracking and accountability throughout the pharmaceutical supply chain. The integrated mobile application creates a robust anti-counterfeiting ecosystem by combining secure QR code authentication with intelligent visual recognition capabilities. The computer vision model provides accurate tablet counting and strip identification, maintaining continuity of medication tracking even when packaging is modified during dispensing processes, thus significantly enhancing supply chain transparency. This dual-authentication approach builds consumer confidence in pharmaceutical authenticity while directly addressing critical vulnerabilities identified in current regulatory frameworks. This comprehensive mobile health solution provides a scalable, evidence-based approach to pharmaceutical authentication that can be readily implemented across diverse healthcare systems globally, offering substantial potential for reducing the circulation of falsified medicines and improving patient safety outcomes in the ongoing battle against the pandemic of counterfeit pharmaceuticals.

The research focus was motivated by the limited understanding of cognitive technologies and the growing gap between artificial intelligence (AI) and human intelligence. The research is a literature review, and its purpose is to simplify the meaning and processes behind cognitive technologies, notably, the fundamentals of machine learning (ML) and computer vision with the intention to briefly address the alleged threat of AI taking over the job market. The research is a review of peer-reviewed articles retrieved from comparative studies, systematic reviews, meta-analysis, service research, reports, conference proceedings, experimental studies, literature reviews, scientometric analyses, books, and multi-case studies, dating from the years of 2018 to 2024. This literature review defines machine learning (ML), artificial intelligence (AI), computer vision, and convolutional neural networks (CNNs). It also compares machine learning to traditional programming and reveals the types of learning in ML models’ training. ML and its correlation with AI are also discussed and details about theory of mind, self-aware AI, reactive machines, and limited memory AI are shared. The literature expounds computer vision, particularly convolutional neural network (CNN) and CNN layers. Recent cutting-edge applications of artificial intelligence including generative AI models and autonomous systems are also incorporated. Finally, the literature briefly addresses the alleged threat of AI taking over the job market. The findings of this literature review reveal that AI is becoming the new way of operating. The conclusion shows that AI models require significant computation to allow computers to learn autonomously. Thus, understanding mathematical models of data and perfecting the process of writing software could be the key to remaining employable as more jobs are expected to be shifted due to AI and tasks automation. Keywords: Cognitive technology, artificial intelligence, machine learning, computer vision, convolutional neural networks

and AI-assisted image manipulation tools, which The emergence of advanced image manipulation techniques in the digital age has made it extremely difficult to detect and categorize forged images. In order to identify and classify image forgeries, this study proposes a deep learning-based method that combines a Convolutional Neural Network (CNN) classifier with the You Only Look Once (YOLO) object detection model. The suggested system uses sophisticated feature extraction techniques to categorize images into authentic, copy-move, spliced, and deepfake forgeries after being trained on a custom dataset of manipulated images. While CNN-based classification offers accurate categorization, our experimental results show how reliable and effective YOLO is at locating forgery regions. Because of its high accuracy, the suggested model is a useful tool for security and digital forensics applications. Keywords: Image Forgery, YOLO, CNN, Deep Learning, Classification, Digital Forensics

Object detection is a fundamental problem in computer vision that involves detecting and localizing objects within an image. In recent years, convolutional neural networks (CNNs) have become the state-of-the-art approach for object detection tasks. YOLO (You Only Look Once) is a popular real-time object detection system that uses a single CNN to predict the bounding boxes and class probabilities for objects in an image. In this paper, we present a human detection and counting system using YOLO. Our system is trained on a large dataset of annotated images and achieves high accuracy and real-time performance. We evaluate our system on a public dataset and demonstrate its effectiveness in various scenarios. This paper focuses on developing a system for human detection and counting using YOLOv4 and Flask API. The system aims to address the increasing importance of human detection and counting in various fields such as social distancing, crowd management, and surveillance.

Cigarettes are processed products from tobacco products which are used by burning and then smoked. Smoking activities are often found in everyday life, including in public infrastructure. The approach taken to prevent this activity generally uses manual information or human intervention. In terms of this approach, there are often many problems and failures due to the lack of manpower and supporting rules. Therefore, this study was structured with the aim of being able to detect smoking objects in real time using the You Only Look Once (YOLO) method. YOLO which is based on deep learning is very good at detecting objects, this model provides a single convolution neural network in assigning location and classification. So that in its application, YOLO is very fast in detecting and recognizing objects. This study conducted experiments on the training dataset in testing the YOLOv3, YOLOv3-Tiny and YOLOv4 models. The best training results were obtained in the YOLOv4 model with a composition of 80% training and 20% validation data sharing with a Mean Average Precision (mAP) of 92.54% and an F1-Score of 0.89. This study also conducted experiments on testing to detect cigarettes in real time, where the system can detect cigarettes up to a distance of 4.5 meters, and the highest detection accuracy is obtained at a distance of 1 meter, namely 99.03%.

YOLO Based Breast Masses Detection and Classification in Full-Field Digital Mammograms