OBJECT DETECTION AND TEXT TO SPEECH CONVERSION BASED ON YOLOV7 USING DEEP LEARNING

Object detection from deep learning has performed better in many applications, and yet real images usually depict challenges such as noise blurring or jitter rotation. Such problems generally degrade the accuracy and the efficiency of object detection systems dramatically. The purpose of this work is to identify objects satisfactorily by applying a method called You Only Look Once (YOLO), which tackles numerous problems while it provides plenty of advantages over other solutions.Unlike other algorithms that are known as Convolutional Neural Networks (CNN)or Fast-Convolutional Neural Networks, process images partially by focusing on specific areas. YOLO is different from that; it looks at an entire image at once while predicting bounding boxes and class probabilities for objects within these boxes by making a single forward pass of the network. This holistic approach makes YOLO significantly faster and much more efficient than other algorithms that process parts of the image separately.For this project, we have used the YOLO algorithm for detecting all types of objects in a real-time scenario. Moreover, an Android Application has been developed incorporating YOLO-based object detection for enhancing user experience. This application does not only detect objects in images or live video feeds but also provides voice feedback to the user. Such an enhancement would make the system much more accessible for individuals with visual impairments because they could receive immediate auditory information about the objects detected in their environment.

This paper provides a comprehensive study of the security of YOLO (You Only Look Once) model series for object detection, emphasizing their evolution, technical innovations, and performance across the COCO dataset. The robustness of YOLO models under adversarial attacks and image corruption, offering insights into their resilience and adaptability, is analyzed in depth. As real-time object detection plays an increasingly vital role in applications such as autonomous driving, security, and surveillance, this review aims to clarify the strengths and limitations of each YOLO iteration, serving as a valuable resource for researchers and practitioners aiming to optimize model selection and deployment in dynamic, real-world environments. The results reveal that YOLOX models, particularly their large variants, exhibit superior robustness compared to other YOLO versions, maintaining higher accuracy under challenging conditions. Our findings serve as a valuable resource for researchers and practitioners aiming to optimize YOLO models for dynamic and adversarial real-world environments while guiding future research toward developing more resilient object detection systems.

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

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.

In this work, we present our intelligent SSD, SeeSSD , an energy-efficient computational SSD for a real-time object detection system. SeeSSD embeds an FPGA-based CNN processing engine and the firmware that performs the convolutional operation on the target image. SeeSSD processes the image data at the storage before sending it to the host. This reduces the amount of data transferred to the host and lowers the data movement overhead, thus reducing transfer time and saving power. By using our SeeSSD system and YOLO_Embed, an object detection neural network model, we are able to outperform the fastest YOLO model for an embedded controller, YOLO-Lite, in terms of performance, accuracy, and energy efficiency. YOLO (You Only Look Once) models are a series of one-stage object detection neural models that have become very popular due to their fast speed and high accuracy. The contribution of this work includes designing and implementing our SeeSSD system with a lightweight object detection model, YOLO_Embed, for reducing the data movement overhead, performing real-time inference, and lowering the overall power consumption. We implemented the entire software stack associated with the SeeSSD system; on-device CNN acceleration engine implemented on FPGA, object identification interface for SeeSSD using YOLO_Embed, and embedded software layer in SeeSSD for on-device convolutional processing. We calculated our YOLO_Embed model’s accuracy on object detection dataset benchmarks such as PASCAL VOC 2012, which came out to be 38.1% mAP (mean Accuracy Precision). Our system was able to perform inference in 0.21 seconds while reducing the power consumption by approximately 1.2 × and 1.4 × for CPU-Only and CPU+GPU systems, respectively. We were also able to reduce the data movement overhead by 24 × for a single target image.

Road objects (such as pedestrians and vehicles) detection is a very important step to enhance road safety and achieve autonomous driving. Many on-vehicle sensors, such as radars, lidars and ultrasonic sensors, are used to detect surrounding objects. However, cameras are widely used sensors for road objects detection for the rich information they provide and their inexpensive prices with compared to other sensors. Machine learning and computer vision algorithms are utilized to classify objects in the collected images and videos. There are many computer vision algorithms proposed for image and video object detection, e.g. logistic regression and SVM with feature extraction. However, Convolutional Neural Network (CNN) al-gorithms showed a high detection accuracy compared to other approaches. This research implements You Only Look Once (YOLO) algorithm that uses Draknet-53 CNN to detect four classes: pedestrians, vehicles, trucks and cyclists. The model is trained using Kitti images dataset which is collected from public roads using vehicle’s front looking camera. The algorithm is tested, and detection results are presented.

The chapter discusses the design and development of a novel object detection model keeping the motive that anyone and everyone with little to no prior knowledge can easily use it. The object detection is a part of computer vision which deals with detection and localization of objects in images or videos. These systems can be implemented for surveillance, keeping track of goods, robotics, medical field, self-driving cars, and much more. A lot of organizations are using it for various purposes such as it is used by NASA for analysis for deep space analysis, self-driving cars use it for making accurate decisions, and airports use it for security and surveillance. Although it has numerous use cases, but because of complex implementation and need of expertise in the field, it is not being widely used currently. In this chapter, we have made an effort to automate most of the process that a user should do in order to train and use models for object detection. The most complex part of using these technologies is training custom models, and only people having expertise in field are doing it. With the system designed by us, anyone would be able to train custom models on their own and they don’t have to do much for it. Normally, first users have to gather large amount of images and then annotate them 124one by one, which is a tedious and time taking process. One has to do this for thousands of images and each of those images can have multiple instances of various objects to train a model to give a good accuracy. We are automating the whole process with the help of AWS recognition module of AWS. You only look once (YOLO) object detection model is used for automation, as it is fast and accurate at the same time which gives it edge over the other models available for use. This model is based on deep learning to learn through the images provided by user in training phase and then later on can be used to perform detection on images or real-time videos. A graphic user interface (GUI) is also developed, so the users can easily interact with the system and train their own custom models and use as they want it to be used. Data are sent over the AWS servers and retrieve the results using a block of python code, then the response collected from AWS is manipulated and the data are extracted that is required to annotate the images and then processing is done according to the need of our target model. This format can be different for various models which can be used for object detection.

Abstract- With the rapid growth of urban populations and the increasing number of vehicles on the road, traffic congestion has become a significant problem in metropolitan areas. Conventional traffic control systems, which rely on pre-set signal timers, often fail to address real-time traffic conditions effectively, leading to inefficient traffic flow, increased fuel consumption, and environmental degradation. The paper “Smart Traffic Control System Based on Traffic Density Using YOLO” introduces a novel approach to address this issue by integrating artificial intelligence and computer vision into traffic management. The proposed system utilizes the YOLO (You Only Look Once) object detection algorithm to detect and classify vehicles in real-time from live video feeds captured at intersections. YOLO, known for its speed and accuracy in object detection tasks, calculates the vehicle density in each direction. Based on the detected density, the system dynamically adjusts the green light duration for each lane, ensuring that the direction with higher traffic receives longer green light periods. This method aims to optimize signal switching and minimize overall vehicle wait time. The paper outlines the complete architecture of the system, which includes modules for video input processing, vehicle detection using YOLO, traffic density computation, and adaptive signal timing control. By leveraging deep learning, the system enhances decision-making capabilities without relying on costly hardware like inductive loops or infrared sensors. This review evaluates the effectiveness and innovation of the proposed system, comparing it with traditional and sensor-based models. It highlights the advantages of using YOLO, such as real-time processing, high accuracy, and scalability. Additionally, the review discusses the system’s limitations, such as dependency on video clarity and environmental conditions, and proposes areas for future enhancement, including integration with cloud computing and edge AI for broader deployment. Keywords- Smart Traffic Control System, YOLO Object Detection, Traffic Density Estimation, Real-Time Traffic Monitoring, Adaptive Traffic Signal Control, Vehicle Classification ( Cars, Bikes, Buses, Trucks, Rikshaws), Intelligent Transportation System.

Aerial object detection plays a vital role in applications such as natural disaster prevention and urban traffic management, thanks to its ability to handle wide coverage areas and diverse objects. As a leading method for this task, You Only Look Once (YOLO) leverages multi-scale feature extraction to detect objects of various sizes. However, most YOLO-based methods focus on feature extraction and fusion from adjacent scales, neglecting the potential collaboration between non-adjacent scales. This limitation leads to redundant parameters and suboptimal detection performance. To address these issues, this paper proposes AF-YOLO (Asymptotic Feature Extraction and Fusion YOLO), a novel approach tailored for aerial object detection. AF-YOLO introduces two lightweight modules: SCC2f and PAFFN. SCC2f, an optimized version of cross-stage partial bottleneck with spatial and channel reconstruction convolution layers, reduces redundancy and enables efficient multi-scale feature extraction. PAFFN, a parallel asymptotic feature fusion network, facilitates enhanced interaction and fusion of non-adjacent scale features. Additionally, AF-YOLO incorporates a P2 layer to improve small object detection and removes YOLO’s P5 layer for a more lightweight design, specifically optimized for aerial detection tasks. Experimental results demonstrate AF-YOLO’s significant improvements across multiple benchmarks: on the VisDrone dataset, it achieves a 6.1% higher mAP<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0.5</sub> compared to recent baselines while using only 41.8% of their parameters; on the DIOR dataset, it shows a 3.3% accuracy improvement over YOLOv8. These quantitative results are further supported by its superior performance on the DOTA and FAIR1M datasets, with additional validation on HazyDet confirming its robustness in adverse weather conditions. Collectively, these achievements highlight AF-YOLO’s exceptional generalization capability and efficient lightweight design, establishing a new state-of-the-art for aerial object detection systems.

The objective of this study is to examine two powerful components: the YOLO (You Only Look Once) framework for object recognition on edge devices and the MobileNet architecture. With the rising demand for real-time, efficient, and accurate object detection in constrained environments, these models offer distinct advantages suited to different applications. YOLO is renowned for its high accuracy and speed, making it ideal for complex detection tasks, whereas MobileNet is optimized for efficiency, enabling deployment on devices with limited computational resources. The paper discusses the evolution of these models, their architectural differences, and their respective strengths and limitations when implemented on edge devices. Furthermore, we explore the feasibility of integrating these frameworks into a hybrid model that leverages the lightweight feature extraction of MobileNet and the robust detection capabilities of YOLO. Such a hybrid approach could significantly enhance real-time object detection performance across a wide range of hardware platforms.&nbsp; Researchers and practitioners seeking to deploy advanced object detection models in real-world applications, particularly where computational efficiency and accuracy are paramount may find this review as a valuable resource.

Object detection is a critical component in intelligent surveillance, disaster response, and autonomous dronebased monitoring systems. In recent years, the You Only Look Once (YOLO) family of algorithms has transformed real-time object detection by achieving an effective balance between detection speed and accuracy. This review paper presents a comprehensive analysis of YOLO models from YOLOv1 to YOLOv8, emphasizing their architectural evolution, backbone development, and feature extraction strategies. Each version’s key innovations are discussed in detail—from YOLOv1’s unified detection framework and YOLOv2’s introduction of anchor boxes to YOLOv8’s advanced anchor-free detection mechanism. The comparative evaluation focuses on model accuracy (mAP), inference speed (FPS), and deployment feasibility for embedded and UAV-based systems. Findings from existing literature and experimental insights reveal that successive YOLO versions have progressively enhanced small object detection, robustness to scale variation, and computational efficiency. Overall, the study concludes that YOLOv8 currently achieves the most optimal trade-off between precision, speed, and resource utilization, establishing it as a leading framework for real-time aerial human detection and a promising foundation for future advancements in autonomous surveillance and rescue applications.

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.

Currently, many object detection systems still use devices with large sizes, such as using PCs, as supporting devices, for object detection. This makes these devices challenging to use as a security system in public facilities based on human object detection. In contrast, many Mini PCs currently use ARM processors with high specifications. In this research, to detect human objects will use the Mini PC Nanopi M4V2 device that has a speed in processing with the support of CPU Dual-Core Cortex-A72 (up to 2.0 GHz) + Cortex A53 (Up to 2.0 GHz) and 4 Gb DDR4 Ram. In addition, for the human object detection system, the author uses the You Only Look Once (YOLO) method with the YoloV4-Tiny type, With these specifications and methods, the detection rate and FPS score are seen which are the feasibility values for use in detecting human objects. The simulation for human object recognition was carried out using recorded video, simulation obtained a detection rate of 0,9845 or 98% with FPS score of 3.81-5.55. These results are the best when compared with the YOLOV4 and YOLOV5 models. With these results, it can be applied in various human detection applications and of course robustness testing is needed.

Object detection is a technology that automatically identifies and locates specific objects in images or videos and plays a core role in various fields, such as autonomous driving, security surveillance, and medical imaging. You Only Look Once (YOLO) has gained attention for achieving both accuracy and detection speed in real-time applications; however, during the resolution reduction process, detailed information is lost, and unnecessary signals are mixed in the multi-scale feature fusion stage, resulting in limited detection performance for small objects and complex background scenes. To alleviate these limitations, we propose YOLO-RECAP, which integrates a Content-Aware ReAssembly of FEatures (CARAFE) and Efficient Channel Attention (ECA) modules based on YOLOv11. CARAFE precisely restores boundaries and shapes during the upsampling stage by utilizing position-specific content information, whereas ECA effectively models interchannel interactions to emphasize important signals. For performance verification, VisDrone2019, Store Keeping Unit-110 K (SKU-110 K), Pascal Visual Object Classes (VOC), and Dataset for Object Detection in Aerial Images (DOTA)v1 were used. In addition, Latency is reported under an end-to-end setting that includes pre-processing, inference, and post-processing, to reflect practical deployment conditions. As a result, YOLO-RECAP achieved mAP50 of 0.316, mAP50@95 of 0.184, Latency of 16.5 ms, and 60.6 FPS on VisDrone2019; mAP50 of 0.895, mAP50@95 of 0.572, Latency of 18.2 ms, and 55.0 FPS on SKU-110 K; mAP50 of 0.770, mAP50@95 of 0.561, Latency of 15.6 ms, and 63.9 FPS on Pascal VOC; and mAP50 of 0.281, mAP50@95 of 0.157, Latency of 18.6 ms, and 53.9 FPS on DOTAv1. Qualitative bounding-box visualizations further indicate reduced missed detections and more stable predictions in cluttered and densely populated scenes. As a result, YOLO-RECAP provided a more stable and balanced detection performance than the existing YOLOv11 and recent detection models, especially for small objects and complex backgrounds. This code is available at https://github.com/Heon-ju/YOLO-RECAP.git .

One popular of object detection model for object detection is You Only Look Once (YOLO) with humans are among the most often utilized for detection objects. Despite the various of human datasets, just a few research that compared the datasets performance against various versions of the YOLO algorithm. This study compares the performance of YOLOv10, YOLOv11, and YOLOv12 on eight different datasets, such as CrowdHuman, CityPersons, Wider Person, Mall Dataset, INRIA, Microsoft Common Object (MS COCO), PASCAL VOC, and MOT17. Precision, recall, mAP@50, and mAP@50-95 are used to measure the YOLO model version's performance on each dataset. The results indicate that each datasets have different perfomance on each version of YOLO, so the performance on model depends on the variation of the dataset. The best results on the MOT17 dataset are obtained by YOLOv12, with 0.909 in precision, 0.775 in recall, 0.88 in mAP@50, and 0.695 in mAP@50-95. On the City Person dataset. However, YOLOv11 performs best result, with 0.782 in precision, 0.529 in recall, 0.694 in mAP@50, and 0.476 in mAP@50-95. Therefore, choosing a YOLO version that is appropriate for the dataset's complexity is essential to creating the best detection model Therefore, selecting the appropriate YOLO version according to the dataset complexity is crucial to obtain the most optimal detection model.