Top Research Papers on Object Detection Using YOLO

This paper compares the performance of the standard state-of-the-art object detectors that were retrained on a dataset of thermal images extracted from videos that simulate illegal movements around the border and in protected areas and presents the results of the recognition of humans and animals in thermal images.

A comprehensive review of single stage object detectors specially YOLOs, regression formulation, their architecture advancements, and performance statistics is presented.

Vision is a major component in several digital technologies and tools used in agriculture. Object detection plays a pivotal role in digital farming by automating the task of detecting, identifying, and localization of various objects in large-scale agrarian landscapes. The single-stage detection algorithm, You Only Look Once (YOLO), has gained popularity in agriculture in a relatively short span due to its state-of-the-art performance in terms of accuracy, speed, and network size. YOLO offers real-time detection performance with good accuracy and is implemented in various agricultural tasks, i...

An improved backbone MHSA-Darknet is designed to retain sufficient global context information and extract more differentiated features for object detection via multi-head self-attention and present a simple yet highly effective weighted bi-directional feature pyramid network (BiFPN) for effectively cross-scale feature fusion.

The proposed GCL-YOLO-S network achieved the highest and second-highest detection accuracy on the two datasets with the smallest parameter amount and a medium calculation amount, respectively.

A modified YOLOv1 based neural network based on an inception model with a convolution kernel of 1 ∗ 1 is added, which reduced the number of weight parameters of the layers and the proposed method achieved better performance.

This paper proposes the Real-Time DEtection TRansformer (RT-DETR), the first real-time end-to-end object detector to the best knowledge that addresses the above dilemma and designs an efficient hybrid encoder to expeditiously process multi-scale features by decoupling intra-scale interaction and cross-scale fusion to improve speed.

The YOLO series has become the most popular framework for real-time object detection due to its reasonable trade-off between speed and accuracy. However, we observe that the speed and accuracy of YOLOs are negatively affected by the NMS. Recently, end-to-end Transformer-based detectors (DETRs) have provided an alternative to eliminating NMS. Nevertheless, the high computational cost limits their practicality and hinders them from fully exploiting the advantage of excluding NMS. In this paper, we propose the Real-Time DEtection TRansformer (RT-DETR), the first real-time end-to-end object detect...

In recent years, YOLO object detection models have undergone significant advancement due to the success of novel deep convolutional networks. The success of these YOLO models is often attributed to their use of guidance techniques, such as expertly tailored deeper backbone and meticulously crafted detector head, which provides effective mechanisms to tradeoff between accuracy and efficiency. However, these sluggish-reasoning models are not capable of handling false detection and negative phenomena, facing challenges include improving the robustness of scaled objects detection against occlude a...

Object detection is one of the most crucial tasks in computer vision and remote sensing to identify specific categories of various objects in images. The unmanned aerial vehicle (UAV)-based thermal infrared (TIR) remote sensing multi-scenario images and videos are two important data sources in public security. However, their object detection process is still challenging because of the complicated scene information, coarse resolution compared with the visible videos and lack of public labelled datasets and training models. This study proposed a UAV TIR object detection framework for images and ...

This paper presents the fundamental overview of object detection methods by including two classes of object detectors, including YOLO v1, v2, v3, and SSD, and its comparison with previous methods for detection and recognition of object is described graphically.

In Automatic Driving System (ADS) and Driver Assistance System (DAS), object detection plays a vital part. Nevertheless, existing real-time detection models for tiny vehicle objects have the problems of low precision and poor performance. To solve these issues, we propose a novel real-time object detection model based on You Only Look Once Version 2 (YOLO-v2) deep learning framework for tiny vehicle objects, called Optimized You Only Look Once Version 2 (O-YOLO-v2). In the proposed model, a new structure is introduced to strengthen the feature extraction ability of the network by adding convol...

This paper proposes an efficient channel attention pyramid YOLO (ECAP-YOLO), which eliminated the module for detecting large objects and added a detect layer to find smaller objects, reducing the computing power used for detecting small targets and improving the detection rate.

A differentiable image processing (DIP) module is presented to take into account the adverse weather conditions for YOLO detector, whose parameters are predicted by a small convolutional neural network (CNN-PP).

An improved infrared image object detection network, YOLO-FIRI, is proposed and an improved attention module is introduced in residual blocks to focus on objects and suppress background and multiscale detection is added to improve the detection accuracy of small objects.

The quantitative experimental results show that compared with other state-of-the-art models, the proposed YOLO-ACN has high accuracy and speed in detecting small targets and occluded objects.

YOLO-World is introduced, an innovative approach that enhances YOLO with open-vocabulary detection capabilities through vision-language modeling and pre-training on large-scale datasets and proposes a new Re-parameterizable Vision-Language Path Aggregation Network (RepVL-PAN) and region-text contrastive loss to facilitate the interaction between visual and linguistic information.

This paper presents a complete survey of YOLO versions up to YOLOv8, and explained about the performance metrics used in object detection, post-processing methods, dataset availability and object detection techniques that are used mostly.

A lite version of FFCA-YOLO (L-FFCA-YOLO) is optimized by reconstructing the backbone and neck of FFCA-YOLO based on partial convolution (PConv).

DAMO-YOLO is extended from YOLO with some new technologies, including Neural Architecture Search (NAS), efficient Reparameterized Generalized-FPN (RepGFPN), a lightweight head with AlignedOTA label assignment, and distillation enhancement to improve performance to a higher level.

The Fire-YOLO detection model expands the feature extraction network from three dimensions, which enhances feature propagation of fire small targets identification, improves network performance, and reduces model parameters.

As autonomous vehicles and autonomous racing rise in popularity, so does the need for faster and more accurate detectors. While our naked eyes are able to extract contextual information almost instantly, even from far away, image resolution and computational resources limitations make detecting smaller objects (that is, objects that occupy a small pixel area in the input image) a genuinely challenging task for machines and a wide-open research field. This study explores how the popular YOLOv5 object detector can be modified to improve its performance in detecting smaller objects, with a partic...

This study investigates the performance degradation of SNNs in a more challenging regression problem (i.e., object detection), and introduces two novel methods: channel-wise normalization and signed neuron with imbalanced threshold, both of which provide fast and accurate information transmission for deep SNN's.

A special detection method for small objects in UAV perspective based on YOLOv3, the Resblock in darknet is first optimized by concatenating two ResNet units that have the same width and height and the entire darknet structure is improved by increasing convolution operation at an early layer to enrich spatial information.

A systematic exploration of the PubMed database to identify peer-reviewed articles published between 2018 and 2023 demonstrates the effectiveness of YOLO in outperforming alternative existing methods for medical object detection and proposes future directions for leveraging the potential of YOLO for medical object detection.

This paper points out the problems related to real-time processing in Y OLO object detection associated with network cameras, and proposes a novel YOLO architecture with adaptive frame control (AFC) that can efficiently cope with these problems.

The Hypergraph Computation Empowered Semantic Collecting and Scattering (HGC-SCS) framework is proposed, which transposes visual feature maps into a semantic space and constructs a hypergraph for high-order message propagation, advancing beyond conventional feature-focused learning.

From this paper, the best model between YOLO model is Yolov4 which had achieved state-of-the-art results with 82.08% AP50 using the custom dataset at a real time speed of around 14 FPS on GTX 1660ti.

We introduce YOLO-pose, a novel heatmap-free approach for joint detection, and 2D multi-person pose estimation in an image based on the popular YOLO object detection framework. Existing heatmap based two-stage approaches are sub-optimal as they are not end-to-end trainable and training relies on a surrogate L1 loss that is not equivalent to maximizing the evaluation metric, i.e. Object Keypoint Similarity (OKS). Our framework allows us to train the model end-to-end and optimize the OKS metric itself. The proposed model learns to jointly detect bounding boxes for multiple persons and their corr...

The YOLO-extract algorithm has a faster convergence speed, reduces the calculation amount by 45.3GFLOPs and the number of parameters by 10.526M, but increases the mAP by 8.1% and the detection speed by 3 times, and is designed to replace CIoU Loss which makes the model bounding box regression faster and the loss lower.

Developing an artificial intelligence-based prostate cancer detection and diagnosis system that can automatically determine important regions and accurately classify the determined regions on an input biopsy image. The Yolo general-purpose object detection algorithm was utilized to detect important regions (for the localization task) and to grade the detected regions (for the classification task). The algorithm was re-trained with our prostate cancer dataset. The dataset was created by annotating 500 real prostate tissue biopsy images. The dataset was split into train/test parts as 450/50 real...

This work investigates and looks at the some of measurements for evaluating the YOLOV5 algorithm of detecting objects and audits the most utilized measurements for object detection and their disparities, applications, and primary ideas.

A stepwisedomain adaptive YOLO (S-DAYOLO) framework is developed which constructs an auxiliary domain to bridge the domain gap and uses a new domain adaptive Y OLO (DAYOLo) in cross-domain object detection tasks.

Industrial defect detection is of great significance to ensure the quality of industrial products. The surface defects of industrial products are characterized by multiple scales, multiple types, abundant small objects, and complex background interference. In particular, small object detection of multiscale defects under complex background interference poses significant challenges for defect detection tasks. How to improve the algorithm's ability to detect industrial defects, especially in enhancing the detection capabilities of small-sized defects, while ensuring that the inference speed is n...

Steel surfaces may exist some defects owing to imperfect manufacturing crafts and external factors, which seriously influence the lifespan and availability of steel. Thus, surface defect detection is a necessary process during industrial production. However, traditional surface defect detection algorithms have the shortcomings of low accuracy and speed. Therefore, we propose a model, named RDD-YOLO, based on YOLOv5 for steel surface defect detection. Firstly, the backbone component is consisted of Res2Net blocks to enlarge the receptive field and extract features of various scales. Secondly, i...

The detection and location of dead trees are extremely important for the management and estimating naturalness of the forests, and timely replanting of dead trees can effectively resist natural disasters and maintain the stability of the ecosystem. Dead trees have the characteristics of small targets and inconspicuous detail information, which leads to the problem of difficult identification. In this paper, we propose a novel lightweight architecture for small objection detection based on the YOLO framework, named LDS-YOLO. Specifically, a novel feature extraction module is proposed, it reuses...

In real life, object detection is widely applied and plays a significant part in the field of computer vision. However, when detecting small objects, the advanced You Only Look Once v4 (YOLO v4) model often misses or incorrectly detects them due to the limited resolution and unclear features of the small objects, which reduces the detection accuracy. A small object detection algorithm based on YOLO v4 and Multi-scale Contextual information and Soft-CIOU loss function, called MCS-YOLO v4, is proposed in this paper. MCS-YOLO v4 adds a scale detection to the existing three scales to obtain rich l...

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 and the Modified Attentional Feature Fusion (AFFM) module aims to better fuse semantic and scale-inconsistent features and to improve accuracy.

A new 1500 image dataset has been created on Indian roads and it detects potholes with a reasonable accuracy on different pothole images and is evaluated based on the mAP, precision and recall.

The experimental results show that the proposed lightweight, single-stage SAR ship target detection model called LMSD-YOLO has a smaller model volume and higher detection accuracy, and can accurately detect multi-scale targets in more complex scenes.