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.