YOLO Vs Faster RCNN for Object Detection and Recognition

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 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 recent years, breakthrough enhancements in computer hardware and supercomputers made object detection a significant topic of research. Accurate object detection models are computationally expensive and are inefficient on simpler and limited configuration settings while faster models achieve real-time speed, work well on simpler configurations but fail to be accurate. There is always a trade-off between speed and accuracy. There is no clear-cut answer on which detector performs the best. The user will have to make a choice based on the requirement. This paper aims at analyzing numerous CNN-based object detection algorithms—R-CNN, Fast R-CNN, Faster R-CNN, You Only Look Once (YOLO), and Single Shot MutliBox Detector (SSD)—and make comparisons concerning performance, precision and speed and state as to which algorithm performs better under certain constraints. This enables the user to pick an object detector of his/her choice that better addresses the demands of an application.

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

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.

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.

Visual tracking is a fundamental technology in computer vision, with wide-ranging applications in fields such as robotics, autonomous driving, augmented reality, and security systems. A key challenge in these domains is ensuring stable object tracking in dynamic and complex environments. Tracking objects in real-world settings presents difficulties like occlusion, lighting changes, and sudden object movements, all of which demand the development of robust algorithms that can handle such variability effectively. This research introduces a novel single object visual tracking technique that leverages the YOLO (You Only Look Once) object detection model, combined with an adaptive particle filter. By integrating YOLO's precise object detection capabilities with the continuous state estimation offered by particle filters, this approach achieves stable object tracking, even in challenging environments. At the heart of the proposed algorithm is a dynamic fusion of YOLO detection results with image feature-based particle weight updates. When YOLO successfully detects an object, the position and size data of the object are used to adjust the state and weights of the particles, resulting in improved tracking accuracy and quick recovery from events such as sudden movements or occlusions. Conversely, in situations where YOLO detection is unavailable, the algorithm seamlessly transitions to a pure particle filter mode. In this fallback mode, visual features, such as image color histograms, are used to update particle weights, ensuring continuous and robust tracking even during temporary detection failures. The object’s dynamic state is modeled using an 8-dimensional vector, which includes information about position, velocity, size, and size-change rate. This comprehensive state representation enables precise tracking of various object motions and size transformations. Additionally, a Bhattacharyya distance-based weight calculation method is employed to assess particle similarity more effectively. Experimental results demonstrate that the proposed method excels in various challenging scenarios, including those with sudden object movements, partial occlusions, and lighting variations. The algorithm consistently delivers stable tracking, underscoring its robustness and adaptability. The contributions of this research have potential applications in fields such as autonomous driving, SLAM (Simultaneous Localization and Mapping), and augmented reality. Specifically, this technique could enhance pedestrian and vehicle tracking in autonomous driving systems and improve the accuracy of dynamic object tracking and environmental mapping in SLAM. It is also likely to play a key role in real-world applications like person tracking in security systems, player motion analysis in sports, and focused tracking in medical imaging. Future research directions include expanding the method to handle multi-object tracking and optimizing its real-time performance, broadening its applicability. Additionally, further improvements in tracking accuracy and robustness may be achieved by incorporating deep learning-based feature extraction and integrating data from various sensors.

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.  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.

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.

Software was developed in Python for the digital processing of object images before their detection using the You Only Look Once (YOLO) artificial neural network. Detection was carried out on object images, specifically vehicles and other road users. The preprocessing of images was performed through histogram equalization and local contrast enhancement. The original images were read from video cameras or graphic files. Image detection was performed using the medium-sized YOLO convolutional neural network model, YOLOv8m. As a result of detection, the studied objects, particularly cars, were highlighted with rectangular bounding boxes. To evaluate the accuracy of object detection, several metrics (parameters) were used: Precision, Recall, Intersection over Union (IoU), F1, and Average Precision (AP). The YOLO network was pre-trained on the Common Objects in Context dataset. The YOLO neural network returns the object recognition confidence, the coordinates of its center (x, y) on the image, and its width and height (w, h). The detected objects are marked with rectangular bounding boxes. The positioning of objects in the images was manually performed using the Roboflow tool, which allows for accurate spatial positioning of each object. The study of the impact of image preprocessing on the accuracy of object detection by the YOLO network was conducted using a typical traffic scene image. After histogram equalization, the histogram becomes more symmetrical and uniform, improving the visual quality of both dark and bright areas. This led to improved object detection accuracy across almost all metrics. The increase in detection accuracy by the IoU parameter is 0.169. The summary improvement in detection accuracy by the IoU, F1, AP parameters is 0.502. It was shown that after image preprocessing, detection accuracy increased, and all vehicles were correctly detected. The developed software can be used in various computer vision systems as well as in Internet of Things systems for remote monitoring and control.

A challenging and attractive task in computer vision is underwater object detection. Although object detection techniques have achieved good performance in general datasets, problems of low visibility and color bias in the complex underwater environment have led to generally poor image quality; besides this, problems with small targets and target aggregation have led to less extractable information, which makes it difficult to achieve satisfactory results. In past research of underwater object detection based on deep learning, most studies have mainly focused on improving detection accuracy by using large networks; the problem of marine underwater lightweight object detection has rarely gotten attention, which has resulted in a large model size and slow detection speed; as such the application of object detection technologies under marine environments needs better real-time and lightweight performance. In view of this, a lightweight underwater object detection method based on the MobileNet v2, You Only Look Once (YOLO) v4 algorithm and attentional feature fusion has been proposed to address this problem, to produce a harmonious balance between accuracy and speediness for target detection in marine environments. In our work, a combination of MobileNet v2 and depth-wise separable convolution is proposed to reduce the number of model parameters and the size of the model. The Modified Attentional Feature Fusion (AFFM) module aims to better fuse semantic and scale-inconsistent features and to improve accuracy. Experiments indicate that the proposed method obtained a mean average precision (mAP) of 81.67% and 92.65% on the PASCAL VOC dataset and the brackish dataset, respectively, and reached a processing speed of 44.22 frame per second (FPS) on the brackish dataset. Moreover, the number of model parameters and the model size were compressed to 16.76% and 19.53% of YOLO v4, respectively, which achieved a good tradeoff between time and accuracy for underwater object detection.

Real‐time inspection and removal of individual Fusarium head blight (FHB) infected corn grains from the processing lines has been a challenging issue due to the bulk handling and smaller kernel size. In this study, four different variants (small(s), medium(m), nano(n), and large(l)) of You Only Look Once (YOLO) version 5 object detection technique were trained for the detection of Fusarium infection in a moving monolayer of touching and non‐touching corn grains. The YOLOv5 object detection models were evaluated for their performance in detecting FHB infection in individual corn grains. A heterogeneous dataset containing images and video frames of healthy and FHB infected corn grains in different illuminations was utilized. The mean average precision calculated at Intersection over Union threshold of .5 (mAP@50) was 99%, 98%, 95%, and 96% for YOLOv5‐s, YOLOv5‐m, YOLOv5‐n, and YOLOv5‐l models, respectively. The detection speed in videos was 3.9, 1.6, 9.8, and .8 frames per second for YOLOv5‐s, m, n, and l models, respectively. For non‐touching grains, all four variants of the YOLOv5 model showed 100% precision, but for touching grains, all variants showed false negatives in detection of FHB infection, especially on overlapping kernels. The recall values were found to be 98%, 99%, 96%, and 97% for YOLOv5‐s, m, n, and l models, respectively. The best combination of mAP, detection speed, and lower false negatives was achieved by the YOLOv5‐m model. YOLOv5‐m has the potential for use in real‐time detection of Fusarium infection in corn grains apart from lag time in videos.Practical ApplicationThe developed video analysis technique based on YOLOv5 object detection method will be beneficial for the accurate identification of Fusarium infected corn grains in bulk handling facilities. The individual FHB infected grains could be detected on processing lines and could be used for real‐time inspection replacing the random sampling techniques currently used, thereby preventing the entry of Fusarium mycotoxins in the food chain. For non touching corn grains, all the YOLOv5 model variants showed a 100% precision in identifying the healthy and FHB infected grains. For touching grains, YOLOv5‐m model showed the best combination of mAP, detection speed, and lower false negatives proving appropriate for inspection on moving conveyor belts. The nano model with the lightweight architecture installed in portable devices can be used for immediate detection of FHB infection without lag time.

Abstract: Artificial intelligence introduced a way to combine machines’ computing ability with human intelligence. Machine learning is a sub-branch of AI that consists of different algorithms to implement concepts of AI in practical terms. But when a machine has to process numerous amounts of data, deep learning algorithms come into the picture. It is observed that when a computing system has to deal with image data then neural network algorithms give efficient ways to process them and draw unique patterns from them. Object detection is the task of identifying required objects from an image. This type of technology plays a crucial role in medical image processing. Some algorithms can efficiently identify and classify objects from an image. It is observed that Fast R-CNN and Faster R-CNN, mask R-CNN, have given pretty good accuracy while performing such tasks. But when time is a concern for a system, such methods put an obstacle of training time and architectural complexity. The You Only Look Once (YOLO) object detection method has introduced a new way of processing images in a single pass. This algorithm is famous because of its speed and correctness. There are numerous models of YOLO developed now which include YOLOv1 to YOLOV8. This paper gives the performance of the latest version of YOLO on the blood cell dataset.

At present, the ship detection of optical remote sensing images based on deep learning has made great progress. However, due to the different use scenarios and specific tasks, how to select an appropriate algorithm according to the characteristics of the target and the target priority, so that achieve the detection goal while consider the detection accuracy and speed, still requires relevant research. In this paper, ship detection methods for optical remote sensing images are studied based on deep learning. First, to meet the needs of ship detection research, according to the characteristics of target size and type, datasets of medium and large ships and small target ships are made, and model training and testing are conducted based on Faster R-CNN, YOLOv4, and SSD algorithms respectively. The actual detection performance of the three algorithms under different ship sizes is obtained. The results show that for medium and large targets, Faster R-CNN has the highest precision, the next is YOLOv4, and SSD is the lowest. The detection speed is that SSD is the fastest, the next is YOLOv4, Faster R-CNN is the slowest. For small target ship detection, YOLOv4 has the best detection accuracy and SSD has the fastest detection speed. Faster R-CNN is not as accurate and fast as the other two algorithms. In addition, for different type ships, the detection results of different algorithms also have some differences. In practical applications, different methods should be used to achieve detection by comprehensively considering such factors as target size, target priority, detection accuracy and speed requirements.

The surrounding of transmission lines is complex, large-scale construction machinery, and smoke and fire pose threats to the circuit facilities and wires. When anomaly occurs, much time is required for the State Grid Corporation to fix it manually. To reduce the inspection burden, we propose a lightweight model running on embedded device to detect foreign objects of transmission lines. Based on the You Only Look Once (YOLO) v3, we use Mobilenetv2 instead of Darknet-53 as the backbone, and use depthwise separable convolution to replace <inline-formula> <tex-math notation="LaTeX">$3\\times $ </tex-math></inline-formula> 3 convolutional kernels in detection head, which greatly reduces the parameter size of the network. And the Fully Convolutional One-Stage Object Detection (FCOS)-like encoding and decode scheme is adopted to reduce network complexity. Meanwhile, in order to compensate for the degradation of accuracy, we have improved data augmentation, learning rate, and loss function. The experiments show that compared with other existed models, the improved YOLOv3 model has a smaller model size and higher detection speed without notably reducing detection accuracy, which has achieved the balance between detection speed and accuracy.