What objects can Yolo detect?
Answers from top 6 papers
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| Papers (6) | Insight |
|---|---|
27 Jun 2016 | Compared to state-of-the-art detection systems, YOLO makes more localization errors but is less likely to predict false positives on background. |
08 Jul 2018 242 Citations | This paper presents a robust and efficient ALPR system based on the state-of-the-art YOLO object detector. |
01 Aug 2018 | In our experiments, YOLO outperforms the other state-of-the-art detector, Faster R-CNN, and our multi-projection YOLO achieves the best accuracy with low-resolution input. |
01 Oct 2017 62 Citations | A new object detection method, OYOLO (Optimized YOLO), is produced, which is 1.18 times faster than YOLO, while outperforming other region-based approaches like R-CNN in accuracy. |
152 Citations | The object detection performance is enhanced in Tinier-YOLO by using the passthrough layer that merges feature maps from the front layers to get fine-grained features, which can counter the negative effect of reducing the model size. |
01 Nov 2018 42 Citations | Due to the One-stage detection model like YOLO having novel structure and great industrial application potential, this paper proposes a new model of detection based on YOLOv2 structure. |
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What is the current advancment in vixtims detection and YOLO?5 answersThe current advancements in object detection, particularly in victim detection, have seen significant progress with the utilization of YOLO-based algorithms. Various studies have focused on enhancing YOLO models for improved victim detection performance. For instance, the RSI-YOLO algorithm introduces channel and spatial attention mechanisms to strengthen feature fusion. Additionally, the YOLO-SWINF model incorporates a 3D-attention module to capture temporal information, enhancing detection results while maintaining real-time processing. Moreover, an auxiliary information-enhanced YOLO model enhances sensitivity and detection performance for small objects, outperforming the original YOLOv5 on challenging datasets. These advancements showcase the continuous evolution of YOLO-based algorithms in improving victim detection capabilities across various applications, including remote sensing, medical, and real-time monitoring.
How good is YOLO for object detection?5 answersYOLO (You Only Look Once) is a highly effective object detection algorithm that has shown significant advancements in the field of computer vision. Various versions of YOLO, such as YOLOv2, YOLOv5, and YOLO-Drone, have been proposed with improvements tailored to different applications. YOLO models have demonstrated superior detection accuracy, precision, recall, and Intersection over Union (IOU) metrics when compared to state-of-the-art detectors. Additionally, YOLO algorithms have been proven to outperform classical object detection methods in terms of detection performance, especially in scenarios involving small objects, remote sensing images, and UAV applications. Despite its strengths, YOLO algorithms may face challenges in handling noisy environments, which can impact their performance.
How can YOLO be used for joint detection and tracking?5 answersYOLO can be used for joint detection and tracking by combining the YOLO object detection algorithm with other tracking algorithms. For example, in the paper by Fan et al., they propose a new algorithm that combines YOLO with the CSR-DCF tracking algorithm. They also add a self-attention mechanism to address the problem of losing targets during tracking in the DCF algorithm. This algorithm demonstrates excellent performance compared to traditional CSR-DCF and DCF target tracking algorithms. Another paper by Zhao et al. modifies YOLO for joint source detection and azimuth estimation in a multi-interfering underwater acoustic environment. They use a modified version of YOLO called M-YOLO, which processes the whole frequency-beam domain sample using a single-regression neural network and directly outputs the target-existence probability and spectrum azimuth. These papers demonstrate how YOLO can be adapted and combined with other algorithms for joint detection and tracking tasks.
How can yolo output can help to reid in object tracking?5 answersThe YOLO algorithm can help with re-identification (reID) in object tracking by providing the output necessary for tracking and connecting target objects over time. YOLO performs classification and bounding box regression in one step, making it faster than most convolutional neural networks. By using YOLO's detection output, the Deep SORT algorithm can be applied for tracking and reID of objects. Deep SORT with Low Confidence Track Filtering (LCF) can filter out low average confidence detections, reducing false positive tracks. This combination of YOLO and Deep SORT with LCF has been shown to improve object tracking performance, especially in scenarios with occlusions and ID switching. Therefore, YOLO's output is crucial for the reID process in object tracking, enabling accurate and efficient tracking of objects over consecutive frames.
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