This review systematically examines the progression of the You Only Look Once (YOLO) object detection algorithms from YOLOv1 to the recently unveiled YOLOv10. Employing a reverse chronological analysis, this study examines the advancements introduced by YOLO algorithms, beginning with YOLOv10 and progressing through YOLOv9, YOLOv8, and subsequent versions to explore each version's contributions to enhancing speed, accuracy, and computational efficiency in real-time object detection. The study highlights the transformative impact of YOLO across five critical application areas: automotive safety, healthcare, industrial manufacturing, surveillance, and agriculture. By detailing the incremental technological advancements that each iteration brought, this review not only chronicles the evolution of YOLO but also discusses the challenges and limitations observed in each earlier versions. The evolution signifies a path towards integrating YOLO with multimodal, context-aware, and General Artificial Intelligence (AGI) systems for the next YOLO decade, promising significant implications for future developments in AI-driven applications. 

YOLOv10 to Its Genesis: A Decadal and Comprehensive Review of The You Only Look Once Series

Object detection, specifically fruitlet detection, is a crucial image processing technique in agricultural automation, enabling the accurate identification of fruitlets on orchard trees within images. It is vital for early fruit load management and overall crop management, facilitating the effective deployment of automation and robotics to optimize orchard productivity and resource use. This study systematically performed an extensive evaluation of the performances of all configurations of YOLOv8, YOLOv9, YOLOv10, and YOLO11 object detection algorithms in terms of precision, recall, mean Average Precision at 50% Intersection over Union (mAP@50), and computational speeds including pre-processing, inference, and post-processing times immature green apple (or fruitlet) detection in commercial orchards. Additionally, this research performed and validated in-field counting of fruitlets using an iPhone and machine vision sensors in 4 different apple varieties (Scifresh, Scilate, Honeycrisp &amp; Cosmic crisp). This investigation of total 22 different configurations of YOLOv8, YOLOv9, YOLOv10 and YOLO11 (5 for YOLOv8, 6 for YOLOv9, 6 for YOLOv10, and 5 for YOLO11) revealed that YOLOv9 gelan-base and YOLO11s outperforms all other configurations of YOLOv10, YOLOv9 and YOLOv8 in terms of mAP@50 with a score of 0.935 and 0.933 respectively. In terms of precision, specifically, YOLOv9 Gelan-e achieved the highest mAP@50 of 0.935, outperforming YOLOv11s's 0.0.933, YOLOv10s’s 0.924, and YOLOv8s's 0.924. In terms of recall, YOLOv9 gelan-base achieved highest value among YOLOv9 configurations (0.899), and YOLO11m performed the best among the YOLO11 configurations (0.897). In comparison for inference speeds, YOLO11n demonstrated fastest inference speeds of only 2.4 ms, while the fastest inference speed across YOLOv10, YOLOv9 and YOLOv8 were 5.5, 11.5 and 4.1 ms for YOLOv10n, YOLOv9 gelan-s and YOLOv8n respectively. 

Comprehensive Performance Evaluation of YOLO11, YOLOv10, YOLOv9 and YOLOv8 on Detecting and Counting Fruitlet in Complex Orchard Environments

Abstract: In order to tackle the ongoing problem of keeping fruits fresh, this research presents an automated freshness detection. The ever-growing presence of surveillance cameras has undoubtedly enhanced public safety. However, manually reviewing vast quantities of video footage for violence detection remains a tedious and error-prone task. This paper proposes a deep learningbased approach for anomaly detection in surveillance videos, with a specific focus on identifying violent activities. Traditional surveillance methods often lack the sophistication to distinguish between normal behaviour and potentially threatening actions. Deep learning offers a significant advantage by automating the identification of deviations from established behavioural patterns in video data. This automation enables real-time analysis of footage, potentially signifying the occurrence of violent incidents and allowing for a swifter response from security personnel. This research delves into the application of deep learning models for violence detection in surveillance footage. We begin by discussing the limitations inherent in conventional methods, such as motion detection and manual review, which struggle to capture the nuances of human behaviour. Subsequently, we explore the advantages that deep learning-based approaches bring to the table, including their ability to learn complex patterns from large datasets and identify subtle anomalies that might escape human observation.

Real-Time Violence Detection in Surveillance Videos Using Deep Learning Approach

The integration of closed-circuit television (CCTV) monitoring is crucial in the field of video processing, which provides an efficient method for comprehensive surveillance. However, a key challenge associated with this practice is its substantial demand for storage space. Typically, surveillance footage is stored in hard disk drives, and due to limited storage spaces, it is deleted after some time. To address this issue, an innovative method for compressing CCTV video, named object detection-based surveillance compression (ODSC), is introduced. Our ODSC model is divided into two steps: -i) depending upon the objects in the video, determine the significant and non-significant frames of surveillance video using the neural network approach YOLOv5s & YOLOv7-tiny and Yolov8s ii) construct the video of significant frames. Following a comprehensive analysis of the experimental outcomes, it is noted that YOLOv8s stands out with a remarkable detection accuracy of 99.7% on the COCO dataset. Our ODSC approach is reducing the storage space greatly and achieving an average compression ratio of up to 96.31% using YOLOv8s, which surpasses the existing state-of-the-art methods.

A Novel Approach for Surveillance Compression using Neural Network Technique

Now-a-days, Object detection algorithms becomes more popular because of their significant contribution to the field of computer vision. Object detection algorithms are divided into two approaches i) region-based approach and ii) region-free approach. In this paper, we implemented YOLOv4 and YOLOv5 techniques of region-free approach due to their high detection speed and accuracy. The objective of this paper is to identify the relevant and non-relevant parts of surveillance videos as watching entire video footage is a time-consuming process. The use case for this research work is ATM surveillance footage, where the dataset is publicly not available, to train the network we developed our data set and then train and compare the proposed models. After experimental results, it is observed that YOLOv5 archives 84% accuracy and give better results than YOLOv4 which achieve 56% accuracy only. 

YOLOv4 Vs YOLOv5: Object Detection on Surveillance Videos

The prevalence of closed-circuit television cameras (CCTV) has witnessed a rapid escalation over recent decades. CCTV serves the purposes of safety, security, and monitoring across various sectors, that contribute to smart infrastructure and sustainable urban development. Though the requirement for CCTV increases, it faces major challenges in surveillance video storage, particularly in high-resolution systems. This results in substantial storage demands, leading to economic and environmental concerns regarding resource utilization and management, aligning with the goals of resource efficiency and sustainable consumption. To address these issues, we propose a frame relevance-based video compression (FRVC) algorithm comprising three phases: (i) dataset preparation, (ii) relevance frame classification, and (iii) video compression. This approach ensures the resilient and efficient management of video data, which enhances its usefulness in urban monitoring. The FRVC framework was tested using a customized surveillance video dataset under three scenarios. In the relevance frame classification module, Mask region-based convolutional neural network (Mask R-CNN) and You Only Look Once version 9 (YOLOv9) object detection approaches are used to detect relevant frames of surveillance video where YOLOv9 surpasses Mask R-CNN in terms of evaluation metrics (accuracy, precision and F1-score) for 80–20 dataset ratio. The proposed FRVC achieves a compression rate up to 96.3%, 99.9% and 63.3% for three different scenarios respectively. Compressed videos maintain the same resolution and frame rate as compared to the original video. This innovative framework supports sustainable technology adoption in the development of surveillance systems, contributing to long-term data storage solutions and cost-effective urban monitoring. 

FRVC: frame relevance based video compression for surveillance videos using deep learning methods

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YOLOv4 Vs YOLOv5: Object Detection on Surveillance Videos

FRVC: frame relevance based video compression for surveillance videos using deep learning methods