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Object detection using deep learning, CNNs and vision transformers: A review
Detecting objects remains one of computer vision and image understanding applications'
most fundamental and challenging aspects. Significant advances in object detection have …
most fundamental and challenging aspects. Significant advances in object detection have …
[HTML][HTML] Deep learning in computer vision: A critical review of emerging techniques and application scenarios
Deep learning has been overwhelmingly successful in computer vision (CV), natural
language processing, and video/speech recognition. In this paper, our focus is on CV. We …
language processing, and video/speech recognition. In this paper, our focus is on CV. We …
Diffusiondet: Diffusion model for object detection
We propose DiffusionDet, a new framework that formulates object detection as a denoising
diffusion process from noisy boxes to object boxes. During the training stage, object boxes …
diffusion process from noisy boxes to object boxes. During the training stage, object boxes …
YOLOv6: A single-stage object detection framework for industrial applications
For years, the YOLO series has been the de facto industry-level standard for efficient object
detection. The YOLO community has prospered overwhelmingly to enrich its use in a …
detection. The YOLO community has prospered overwhelmingly to enrich its use in a …
Towards large-scale small object detection: Survey and benchmarks
With the rise of deep convolutional neural networks, object detection has achieved
prominent advances in past years. However, such prosperity could not camouflage the …
prominent advances in past years. However, such prosperity could not camouflage the …
Mpdiou: a loss for efficient and accurate bounding box regression
S Ma, Y Xu - arxiv preprint arxiv:2307.07662, 2023 - arxiv.org
Bounding box regression (BBR) has been widely used in object detection and instance
segmentation, which is an important step in object localization. However, most of the existing …
segmentation, which is an important step in object localization. However, most of the existing …
Mmrotate: A rotated object detection benchmark using pytorch
We present an open-source toolbox, named MMRotate, which provides a coherent algorithm
framework of training, inferring, and evaluation for the popular rotated object detection …
framework of training, inferring, and evaluation for the popular rotated object detection …
TPH-YOLOv5: Improved YOLOv5 based on transformer prediction head for object detection on drone-captured scenarios
Object detection on drone-captured scenarios is a recent popular task. As drones always
navigate in different altitudes, the object scale varies violently, which burdens the …
navigate in different altitudes, the object scale varies violently, which burdens the …
A normalized Gaussian Wasserstein distance for tiny object detection
Detecting tiny objects is a very challenging problem since a tiny object only contains a few
pixels in size. We demonstrate that state-of-the-art detectors do not produce satisfactory …
pixels in size. We demonstrate that state-of-the-art detectors do not produce satisfactory …
Oriented R-CNN for object detection
Current state-of-the-art two-stage detectors generate oriented proposals through time-
consuming schemes. This diminishes the detectors' speed, thereby becoming the …
consuming schemes. This diminishes the detectors' speed, thereby becoming the …