دورية أكاديمية
Detection of pedestrians and vehicles in autonomous driving with selective kernel networks
| العنوان: | Detection of pedestrians and vehicles in autonomous driving with selective kernel networks |
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| المؤلفون: | Zhenlin Zhang, Gao Hanwen, Xingang Wu |
| المصدر: | Cognitive Computation and Systems, Vol 5, Iss 1, Pp 64-70 (2023) |
| بيانات النشر: | Wiley, 2023. |
| سنة النشر: | 2023 |
| المجموعة: | LCC:Computer engineering. Computer hardware LCC:Computer applications to medicine. Medical informatics |
| مصطلحات موضوعية: | artificial intelligence, computer vision, image processing, Computer engineering. Computer hardware, TK7885-7895, Computer applications to medicine. Medical informatics, R858-859.7 |
| الوصف: | Abstract Accurate detection of pedestrians and vehicles on the road is an important content in autonomous driving technology. In this article, a method to optimise the object detection network using the channel attention mechanism is proposed. In general, small object detection problems and difficult sample detection problems in object detection tasks can be solved by using feature pyramids. Different from building a feature pyramid, the authors did not make extensive changes to the network, but used the channel attention mechanism to dynamically adjust the output of a layer during the feature extraction process, allowing each neuron to adjust its receptive field size adaptively according to multiple scales of the input information, so that the network pays attention to the extraction of important features, especially the features of small objects and difficult samples. In order to evaluate the performance of the proposed method, experiments were conducted on standard benchmark data sets. It has been observed that the proposed method is superior to the original object detection network in terms of the detection accuracy of pedestrians and vehicles, especially the detection of small objects. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2517-7567 |
| العلاقة: | https://doaj.org/toc/2517-7567Test |
| DOI: | 10.1049/ccs2.12078 |
| الوصول الحر: | https://doaj.org/article/7546028745e747758ed56d2331fe63c4Test |
| رقم الانضمام: | edsdoj.7546028745e747758ed56d2331fe63c4 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 25177567 |
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| DOI: | 10.1049/ccs2.12078 |