How to improve object detection?
Answers from top 16 papers
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| Papers (16) | Insight |
|---|---|
19 Aug 2016 40 Citations | Our new region-based detection technique significantly reduces the computational complexity and improves the performance in object detection. |
01 Dec 2010 | The results show that both the object-detection as well as the object-segmentation method are successful and outperform existing methods. |
| An enhancement to one of the existing visual object detection approaches is proposed for generating candidate windows that improves detection accuracy at no additional computational cost. | |
01 Mar 2020 | Our experiments indicate that conventional object detectors tend to be spatially overconfident and thus perform poorly on the task of probabilistic object detection. |
01 Apr 2019 | We show that precise object detection in such scenes remains a challenging frontier even for state-of-the-art object detectors. |
| As a result the quality of object detection can be improved significantly. | |
01 Jun 2018 | Furthermore, since our method is based on scores and bounding boxes without modification on the architecture of object detector, it can be easily applied to off-the-shelf modern object detection frameworks. |
23 Jun 2013 | Consequently, the object detection problem is transformed into searching an image sub-area for maximum local appearance probability, thereby making EMAS an order of magnitude faster than the traditional detection methods. |
| However, due to the large variation in object scale, aspect ratio, and arbitrary orientation, the detection performance is difficult to be further improved. | |
13 Apr 2021 55 Citations | We propose an object detection method using context for improving accuracy of detecting small objects. |
| The experimental results show that our object detection approach achieves real-time performance and good object detection results. | |
| In this paper, we propose a novel deep CNN-based framework to improve object detection performance. | |
01 Jan 2006 | Evaluation on the recent benchmark for object detection confirms the superior performance of our method compared to the state-of-the-art. |
| Experiments on face and pedestrian detection demonstrate that our methods have superior detection performance than some of the state-of-the-art object detectors. | |
06 May 2013 | The results suggest a significant improvement over static object detection. |
| Experimental results show that the model can achieve high accuracy in predicting the performance of object detection. |
Related Questions
How does image enhancement help with object detection?5 answersImage enhancement plays a crucial role in improving object detection accuracy in visually degraded scenes. By integrating image enhancement with object detection, the detection network can be refined using additional enhancement branches, optimizing features to guide learning in object detection tasks. This approach ensures that the detection network is aware of both image quality and object detection, significantly enhancing performance in challenging conditions like underwater, hazy, and low-light environments. Techniques like using foggy images with clear object areas as ground-truth for training image enhancement models have shown effectiveness in focusing dehazing operations on target areas, leading to improved object detection accuracy in complex weather scenarios. Overall, the synergy between image enhancement and object detection is vital for overcoming visual degradation challenges and enhancing detection performance in various scenarios.
How can small object segmentation be improved?4 answersSmall object segmentation can be improved by incorporating attention mechanisms and enhancing feature extraction. Attention-based methods, such as Across Feature Map Attention (AFMA), can quantify the inner-relationship between small and large objects, compensating for the loss of high-level feature information of small objects. Additionally, using lightweight object detection algorithms with attention enhancement can improve the accuracy and efficiency of small object detection. These algorithms utilize shallower scale features, introduce additional injection paths, and modify spatial and channel weights of scale features. Another approach is to use specific modules like the BoT3 block and coordinate attention mechanism to improve the ability of network feature extraction and fusion. Furthermore, techniques like using RPN module for object framing and selection, FSRCNN for image enlargement, and U-shaped segmentation models can also enhance the accuracy of small object segmentation.
How to improve attention?5 answersAttention can be improved through various methods. One approach is through neurofeedback training, which uses real-time feedback of brain activity to enhance attention adjustment abilities. Individualized neurofeedback training based on functional near-infrared spectroscopy (fNIRS) has been shown to effectively improve attention levels. Engaging in activities such as crochet has also been found to positively affect attention networks and global cortical functioning networks in the brain, leading to increased attention and improved information exchange between brain areas. Another method is through the use of neurofeedback games, such as the Cog-Car, which encourage individuals to use neurofeedback in tangible games and have shown to improve focus and attention. Additionally, repetition of specific tasks that involve attention networks or engaging in mindfulness meditation can also improve attention and produce specific brain changes. Finally, focused training, where individuals are required to focus on a fixed point for an extended period, has been found to improve attention, particularly focused attention and selective attention.
How can Yolov7 be used to improve the performance of object detection?5 answersYOLOv7 can be used to improve the performance of object detection in several ways. Firstly, the HPS-YOLOv7 algorithm proposes a modified high-efficiency layer aggregation network for feature extraction, solving the convergence problem of depth models and enhancing model capacity. Secondly, experimenting with different hyperparameters and ensembling strategies, such as vertically flipping images during training and combining models using Weighted Box Fusion (WBF) prediction, can significantly improve detection precision. Additionally, the YOLOv7 algorithm can be specifically designed for the blind community, incorporating text-to-speech technology to provide voice-guidance for visually impaired individuals, empowering them to identify objects independently. Lastly, in industrial AR applications, YOLOv7 can be improved by adding coordinate attention, using the SIoU loss function, and introducing Adaptive-NMS to adjust the threshold adaptively based on object density, resulting in higher detection accuracy for industrial equipment.
How can the accuracy of object detection be improved in real-time?5 answersReal-time object detection accuracy can be improved through various methods. One approach is to utilize deep learning techniques such as convolutional neural networks (CNNs) to extract discriminative features that effectively capture object appearance and context. Another method is to employ advanced algorithms like the Single Shot Detector (SSD) and You Only Look Once (YOLO) architectures, which allow for high frame rates and real-time object detection. Hardware acceleration strategies such as graphics processing units (GPUs) and field-programmable gate arrays (FPGAs) can also be used to enhance real-time performance. Additionally, combining computer vision techniques like tracking and counting with machine learning algorithms can further improve accuracy and efficiency. The latest version of YOLO, YOLOv5, incorporates a feature pyramid network (FPN) and anchor boxes to enhance object detection accuracy. Finally, a co-design methodology for hardware and software can be employed to optimize the performance of object detection algorithms on heterogeneous platforms.
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