PEMANTAUAN DAN DETEKSI SUHU AYAM PETELUR PADA KANDANG TERTUTUP BERBASIS IOT

This research developed a prototype system for monitoring and detecting the temperature of laying hens in closed cages using the YOLOV5 model, which was then displayed via the web. This system is designed to overcome the challenges of maintaining optimal temperatures in the laying hencage, which is critical for the health and productivity of the hens. Chickens are one of the biggest contributors to these food needs. However, chicken farmers are currently still unable to develop their farms to be able to keep up with the increasing demand, this is due to the fact that many chicken farming systems have not been optimal in developing their farming systems, oneexample is monitoring the temperature of chickens which canaffect sick chickens. Manual inspections are still carried out if disease transmission occurs within a short time to other healthy chickens before handling, whichcan affect the productivity of chicken farmers. Therefore weneeda technology that is able to supervise and monitor chickens by paying attention to symptoms of disease including chicken movement by utilizing image processing with the YOLO algorithm using YOLO V5 technology, this system is able to detect chickens in real-time and measure their body temperature using an integrated infrared sensor. The temperature data obtained from each chicken is processed and analyzed to ensure that thetemperature in the cage is at a safe and comfortable level. Theresultsoftemperature detection and monitoring are then displayed via auser-friendlyweb interface, allowing farmers to access information easily andquickly.This prototype is expected to increase the efficiency of closed cage management, reduce the risk of thermal stress in chickens, and increase egg production results

Modified-vehicle detection and localization model for autonomous vehicle traffic system

The modification of vehicles for financial gain is an evolving tendency observed in India. Recognizing and detecting of these modified illicit cars is an important but critical task in autonomous vehicles (AV). It is always possible for a cyclist or pedestrian to traverse obstacles or other fixed objects that appear in front of any moving vehicle. Vehicles that are autonomous or self-driving require a different system to quickly identify both stationary and moving objects. A deep learning model named you only look once version 5 (YOLOv5)-convolutional block attention module (CBAM) is proposed here for the Indian traffic system which is based on YOLOv5m. The proposed algorithm, YOLOv5-CBAM, has three major components. The first module, the backbone module is employed for feature extraction. The second module is to detect static as well as dynamic objects at the same time and the third CBAM module is adopted in the backbone and neck part, which mainly focuses on the more prominent features. Two cross stage partial (CSP) modules were used after every convolutional layer resulting in an additional head to the proposed model. Four head modules equipped with anchor boxes performed the final detection. For the present dataset, the proposed model showed 98.2% mean average precision (mAP), 98.4% precision, and 94.8% recall as compared to the original YOLOv5m

IMPLEMENTASI YOLOV5 UNTUK DETEKSI KARTU DEBIT: STUDI KASUS PADA KLASIFIKASI BRITAMA DAN SIMPEDES

This study aims to develop an object detection model based on YOLOv5 to classify debit card types. With the advancement of financial technology, the need for automated systems to identify debit cards has become essential to enhance transaction efficiency and security. The research methodology involves five main stages: dataset collection, data preprocessing through labeling and resizing to 640 x 640, dataset augmentation, YOLOv5 model training, and model evaluation. The dataset used consists of three categories of debit cards, with a total of 300 images. The results demonstrate that the YOLOv5 model achieves excellent performance with a mean average precision (mAP) of 92.7% and an object loss value of 0.08. The high mAP value indicates the model’s capability to accurately recognize objects, while the low object loss value reflects minimal detection errors during testing. In conclusion, YOLOv5 has proven to be reliable for application in debit card detection systems. This study provides significant contributions to the development of automation systems in the financial sector, particularly in improving the efficiency and accuracy of identification processes. It is hoped that this research will serve as a foundation for further studies with broader datasets, the application of more advanced augmentation techniques, and the utilization of more sophisticated hardware to enhance model performance

Comparison of Faster R-CNN and YOLOv5 for Overlapping Objects Recognition

يعد تصنيف الجسم المتداخل أحد التحديات الرئيسية التي يواجهها الباحثون الذين يعملون في اكتشاف الأشياء والتعرف عليها. معظم الخوارزميات المتاحة التي تم تطويرها قادرة فقط على تصنيف أو التعرف على الأشياء التي تكون إما منفصلة بشكل فردي عن بعضها البعض أوجسم  واحد في مشهد (مشاهد) ، ولكن لا تتداخل مع اجسام  أدوات المطبخ. في هذا المشروع ، تم اقتراح خوارزميات Faster R-CNN و YOLOv5 لاكتشاف وتصنيف جسم متداخل في منطقة المطبخ. تم تطبيق YOLOv5 و Faster R-CNN على االاجسام المتداخلة حيث من المتوقع أن يتمكن المرشح أو النواة من فصل االجسم المتداخل في الطبقة المخصصة لتطبيق النماذج. تم استخدام قاعدة بيانات الصور المعيارية لأدوات المطبخ وأدوات المطبخ المتداخلة من الإنترنتااجسام مرجعية أساسية. تم تعيين مجموعات التقييم والتدريب / التحقق عند 20٪ و 80٪ على التوالي. قام هذا المشروع بتقييم أداء هذه التقنيات وتحليل نقاط قوتها وسرعاتها بناءً على الدقة والدقة ودرجةF1.  خلصت نتائج التحليل في هذا المشروع إلى أن YOLOv5 ينتج مربعات إحاطة دقيقة بينما يكتشف Faster R-CNN المزيد من االاجسام. في بيئة اختبار مماثلة ، يُظهر YOLOv5 أداءً أفضل من خوارزمية R-CNN الأسرع. بعد التشغيل في نفس البيئة، حصل هذا المشروع على دقة 0.8912 (89.12٪) لـ YOLOv5 و 0.8392 (83.92٪) لـ Faster R-CNN ، بينما كانت قيمة الخسارة 0.1852 لـ YOLOv5 و 0.2166 لأسرع  R-CNN. تعد المقارنة بين هاتين الطريقتين هي الأكثر حداثة ولم يتم تطبيقها مطلقًا في الكائنات المتداخلة وخاصة أدوات المطبخ.Classifying an overlapping object is one of the main challenges faced by researchers who work in object detection and recognition. Most of the available algorithms that have been developed are only able to classify or recognize objects which are either individually separated from each other or a single object in a scene(s), but not overlapping kitchen utensil objects. In this project, Faster R-CNN and YOLOv5 algorithms were proposed to detect and classify an overlapping object in a kitchen area.  The YOLOv5 and Faster R-CNN were applied to overlapping objects where the filter or kernel that are expected to be able to separate the overlapping object in the dedicated layer of applying models. A kitchen utensil benchmark image database and overlapping kitchen utensils from internet were used as base benchmark objects. The evaluation and training/validation sets are set at 20% and 80% respectively. This project evaluated the performance of these techniques and analyzed their strengths and speeds based on accuracy, precision and F1 score. The analysis results in this project concluded that the YOLOv5 produces accurate bounding boxes whereas the Faster R-CNN detects more objects. In an identical testing environment, YOLOv5 shows the better performance than Faster R-CNN algorithm. After running in the same environment, this project gained the accuracy of 0.8912(89.12%) for YOLOv5 and 0.8392 (83.92%) for Faster R-CNN, while the loss value was 0.1852 for YOLOv5 and 0.2166 for Faster R-CNN. The comparison of these two methods is most current and never been applied in overlapping objects, especially kitchen utensils

ARTIFICIAL INTELLIGENCE BASED HELIPAD DETECTION WITH CONVOLUTIONAL NEURAL NETWORK

When a malfunction occurs in the helicopter or the pilot faints during a flight or performing a duty, and in order to ensure the safety of the pilot and the helicopter, a system must be available to detect the helicopter landing pads, so that the helicopter can land at the airport. Closest safe place immediately. This study focuses on helicopter landing pad detection using YOLOv8 and YOLOv5 models. A dataset of 1877 images collected from the Internet was used to evaluate the performance of the models. YOLOv8 showed good performance in helipad detection with 96.7% accuracy and 95.8% recall, resulting in an average accuracy ([email protected]) of 98.8%. As for YOLOv5, it reached 95.1% precision, 95.8% recall, and 97.5% [email protected]. Both models showed good results, but YOLOv8 outperformed it by a small percent

Evaluating YOLOv5 and YOLOv8: Advancements in Human Detection

The YOLO (You Only Look Once) method is a state-of-the-art approach in real- time object detection, known for its high-speed image processing capabilities. Recently YOLO versions have differed in performance, particularly in terms of detection accuracy and computational efficiency. The objective of this study is to assess the effectiveness and performance of YOLOv5 and YOLOv8 in real-time human detection applications using the SEMMA (Sample, Explore, Modify, Model, and Assess) methodology also. The dataset was processed through the Roboflow platform, which facilitated both the dataset management and the labeling process. Roboflow's tools streamlined the annotation of images, ensuring consistent labeling for deep learning model training and evaluation. F1 score, recall score, and precision score are compared both YOLOv5 and YOLOv8 to evaluate the performance of these architectures. The result of the evaluations shows that the performance of the YOLOv8 is better than the YOLOv5 which, YOLOv5 achieved F1-score equal 0.5865 (58%), recall score equal 0.83 (83%), and precision score of 0.4535 (45%). Meanwhile, YOLOv8 demonstrated better performance, with F1-score of 0.7921 (79%), recall score of 0.8289 (82%), and precision score of 0.7585 (75%). Base on the evaluations, we concluded that the performance of the YOLOv8 model is greater than the YOLOv5 model for Precision, and F1-Score, while YOLOv5 has slightly better score on recall. The contribution of this study is going to implemented into Audio guidance for the blind’s prototype that have been developing in previous study

Model Compression Methods for YOLOv5: A Review

Over the past few years, extensive research has been devoted to enhancing YOLO object detectors. Since its introduction, eight major versions of YOLO have been introduced with the purpose of improving its accuracy and efficiency. While the evident merits of YOLO have yielded to its extensive use in many areas, deploying it on resource-limited devices poses challenges. To address this issue, various neural network compression methods have been developed, which fall under three main categories, namely network pruning, quantization, and knowledge distillation. The fruitful outcomes of utilizing model compression methods, such as lowering memory usage and inference time, make them favorable, if not necessary, for deploying large neural networks on hardware-constrained edge devices. In this review paper, our focus is on pruning and quantization due to their comparative modularity. We categorize them and analyze the practical results of applying those methods to YOLOv5. By doing so, we identify gaps in adapting pruning and quantization for compressing YOLOv5, and provide future directions in this area for further exploration. Among several versions of YOLO, we specifically choose YOLOv5 for its excellent trade-off between recency and popularity in literature. This is the first specific review paper that surveys pruning and quantization methods from an implementation point of view on YOLOv5. Our study is also extendable to newer versions of YOLO as implementing them on resource-limited devices poses the same challenges that persist even today. This paper targets those interested in the practical deployment of model compression methods on YOLOv5, and in exploring different compression techniques that can be used for subsequent versions of YOLO.Comment: 18 pages, 7 Figure

Design and implementation monitoring robotic system based on you only look once model using deep learning technique

The need for robotics systems has become an urgent necessity in various fields, especially in video surveillance and live broadcasting systems. This work is aimed to design and implement a robotic system which is based mainly on raspberry pi 4 model B to control this overall system and display a live video by using a webcam (USB camera) as well as using (YOLOv5) you only look once algorithm-version five a deep learning-based object detector to detect, recognize and display objects in real-time. This deep learning algorithm is highly accurate and fast and is implemented by Python, OpenCV, PyTorch codes and the Context Object Detection Task (COCO) 2020 dataset. This robot can move in all directions and in different places especially in undesirable places to transmit live video with a moving camera and process it by the YOLOv5 model. Also, the robot system can receive images, videos, or YouTube links and process them with YOLOv5. Raspberry Pi is controlled remotely by connecting to the network through Wi-Fi locally or publicly using the internet with a remote desktop connection application. The results were very satisfactory and proved the high-performance efficiency of the robot

CHEATING DETECTION IN ONLINE EXAMS BASED ON CAPTURED VIDEO USING DEEP LEARNING

Today, e-learning has become a reality and a global trend imposed and accelerated by the COVID-19 pandemic. However, there are many risks and challenges related to the credibility of online exams which are of widespread concern to educational institutions around the world. Online exam system continues to gain popularity, particularly during the pandemic, due to the rapid expansion of digitalization and globalization. To protect the integrity of the examination and provide objective and fair results, cheating detection and prevention in examination systems is a must. Therefore, the main objective of this thesis is to develop an effective way of detection of cheating in online exams. In this work, a system to track and prevent attempts to cheat on online exams is developed using artificial intelligence techniques. The suggested solution uses the webcam that is already connected to the computer to record videos of the examinee in real time and afterwards analyze them using different deep learning methods to find best combinations of models for face detection and classification if cheating/not cheating occurred. To evaluate the system, we use a benchmark dataset of exam videos from 24 participants who represented examinees in online exam. An object detection technique is used to detect face appeared in the image and crop the face portion, and then a deep learning based classification model is trained from the images to classify a face as cheating or not cheating. We have proposed an effective combination of data preprocessing, object detection, and classification models to obtain high detection accuracy. We believe that the suggested invigilation methodology can be used in colleges, institutions, and schools to look for and keep an eye on suspicious student behavior. Hopefully, by putting the proposed invigilation method into place, we can aid in eliminating and reducing cheating incidences as it undermines the integrity and fairness of the educational system

Comparative Study of Activation Functions and Their Impact on the YOLOv5 Object Detection Model

Object detection is an important aspect of computer vision research, involving determining the location and class of objects within a scene. For an object detection system to run in real-time, it is vital to minimise the computational costs while maintaining an acceptably high accuracy. In a Convolutional Neural Network (CNN) there is a direct correlation between the accuracy and the computational cost incurred by increasing the number of layers. Activation functions play a key role in a CNN to utilise nonlinearity to help balance the computational cost and accuracy. In this paper, a series of improvements are proposed to the state-of-the-art one-stage real-time object detection model, YOLOv5, providing the capability to enhance the overall performance. The validity of replacing the current activation function in YOLOv5, Swish, with a variety of alternative activation functions was investigated to aid in improving the accuracy and lowering the computational costs associated with visual object detection. This research demonstrates the various improvements in accuracy and performance that are achievable by appropriately selecting a suitable activation function to use in YOLOv5, including ACON, FReLU and Hardswish. The improved YOLOv5 model was verified utilising transfer learning on the German Traffic Sign Detection Benchmark (GTSDB) achieving state-of-the-art performance