What objects can Yolo detect?
Answers from top 6 papers
More filters
| 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. |
Related Questions
How to combe YOLO detection with reid for tracking?5 answersTo combine YOLO detection with reid for tracking, an approach is to integrate the attention module CBAM with the Neck part of the YOLOv5s network to improve the target detection model's ability to extract small object features and enhance recognition ability. Additionally, the Market-1501 dataset can be used to train the pedestrian re-id model, and the VOC dataset can be used to train the detection model specifically for detecting pedestrians. Finally, the improved YOLOv5s detector can be connected with DeepSORT, and a virtual detection line can be set in the video to count the flow of people. This approach has shown improved accuracy compared to the original algorithm and achieved a high detection rate in test videos.
How can YOLO detection be improved with a branch for re-identification (ReID) for tracking?4 answersYOLO detection can be improved with a branch for re-identification (ReID) for tracking. One approach is to introduce a lightweight CARAFE module for up-sampling on the neck layer, which reduces complexity and improves the learning ability of the network. Another method is to design a transformer encoder layer (TE-Layer) and a convolutional block attention module (CBAM) to capture global and rich contextual information, and implement cross-domain object detection through knowledge distillation. Additionally, a context enhancement module can be used to capture global and local information of the image, and high- and low-frequency information decomposed by wavelet transform can be introduced for multi-scale feature fusion. These improvements enhance the sensitivity and detection performance of YOLOv5, making it better suited for tracking and re-identification tasks.
What is accuracy in yolo to detect object?5 answersAccuracy in YOLO for object detection refers to the ability of the algorithm to correctly identify and locate objects in an image. Several papers have discussed the accuracy of different versions of YOLO. Safaldin et al. proposed an improved YOLO-v2 for detecting tiny objects and achieved a detection accuracy of 95.8%. Another paper introduced ACP-YOLO, an enhanced version of YOLOv4, which achieved an average precision of 45.6% and surpassed YOLOv4 in terms of accuracy. Additionally, a paper on SuperYOLO demonstrated its accuracy in small object detection, achieving an accuracy of 73.61% on the VEDAI RS dataset. These papers highlight the advancements made in improving the accuracy of YOLO for object detection.
How can we make license plate recognition using YOLO more efficient?5 answersTo make license plate recognition using YOLO more efficient, several methods have been proposed in the literature. One approach is to add an improved channel attention mechanism to the down-sampling process of YOLOv5, which enhances the feature extraction ability of the model. Another method is to reduce the number of parameters on the input side and set only one class in the YOLO layer, which improves the efficiency and accuracy of the detector for locating license plates. Additionally, using Gated recurrent units (GRU) + Connectionist temporal classification (CTC) for character segmentation-free recognition can significantly shorten the training time and improve the convergence speed and recognition accuracy of the network. Another approach is to propose an end-to-end license plate optimization recognition algorithm based on YOLOv3, which includes a detection dewarping convolutional neural network (DU-CNN) to improve the recognition ability of license plates. These methods have shown better recognition accuracy, improved efficiency, and stability in license plate recognition tasks.
Why YOLO-based road monitoring systems help in real-time vehicle detection?5 answersYOLO-based road monitoring systems are beneficial for real-time vehicle detection due to their ability to track, identify, and categorize vehicles, count the number of vehicles, and classify them accurately and efficiently. The YOLO architecture, combined with OpenCV, enhances vehicle detection accuracy and execution time, even in challenging traffic patterns, through techniques such as transfer learning and extensive dataset collection. Additionally, the use of YOLO and OpenCV trackers, such as CSRT and KCF, has shown superior performance in vehicle detection, tracking, and counting accuracy, achieving high percentages of accuracy in both real-time traffic video datasets and specific KNUVDB datasets. Furthermore, the proposed MEB-YOLO model, incorporating data augmentation, attention mechanisms, and feature fusion, has demonstrated improved detection results on public datasets and real traffic monitoring data, making it efficient for detecting road targets.
What is YOLO?2 answersYOLO (You Only Look Once) is a popular algorithm in machine learning that is used for target detection. It has rapidly developed with several versions available. Each version has its own unique features and application areas. YOLO is known for its fast detection speed and high accuracy compared to previous algorithms. It is widely used in various sectors such as autonomous driving, video surveillance, face recognition, and more. YOLO models are designed to detect different objects under different circumstances, making them versatile. The algorithm has been applied in accident detection systems, crop pathology identification, and industrial surface defect detection. YOLO's evolution has focused on real-time performance and high classification accuracy, making it suitable for deployment on constrained edge devices.