دورية أكاديمية
Underwater object detection algorithm based on attention mechanism and cross-stage partial fast spatial pyramidal pooling
| العنوان: | Underwater object detection algorithm based on attention mechanism and cross-stage partial fast spatial pyramidal pooling |
|---|---|
| المؤلفون: | Jinghui Yan, Zhuang Zhou, Dujuan Zhou, Binghua Su, Zhe Xuanyuan, Jialin Tang, Yunting Lai, Jiongjiang Chen, Wanxin Liang |
| المصدر: | Frontiers in Marine Science, Vol 9 (2022) |
| بيانات النشر: | Frontiers Media S.A., 2022. |
| سنة النشر: | 2022 |
| المجموعة: | LCC:Science LCC:General. Including nature conservation, geographical distribution |
| مصطلحات موضوعية: | Underwater Object detection, ACFP-YOLO, YOLOv7, attention, SPPFCSPC, Science, General. Including nature conservation, geographical distribution, QH1-199.5 |
| الوصف: | For the routine target detection algorithm in the underwater complex environment to obtain the image of the existence of blurred images, complex background and other phenomena, leading to difficulties in model feature extraction, target miss detection and other problems. Meanwhile, an improved YOLOv7 model is proposed in order to improve the accuracy and real-time performance of the underwater target detection model. The improved model is based on the single-stage target detection model YOLOv7, incorporating the CBAM attention mechanism in the model, so that the feature information of the detection target is weighted and enhanced in the spatial dimension and the channel dimension, capturing the local relevance of feature information, making the model more focused on target feature information, improved detection accuracy, and using the SPPFCSPC module, reducing the computational effort of the model while keeping the model perceptual field unchanged, improved inference speed of the model. After a large number of comparison experiments and ablation experiments, it is proved that our proposed ACFP-YOLO algorithm model has higher detection accuracy compared with Efficientdet, Faster-RCNN, SSD, YOLOv3, YOLOv4, YOLOv5 models and the latest YOLOv7 model, and is more accurate for target detection tasks in complex underwater environments advantages. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2296-7745 |
| Relation: | https://www.frontiersin.org/articles/10.3389/fmars.2022.1056300/full; https://doaj.org/toc/2296-7745 |
| DOI: | 10.3389/fmars.2022.1056300 |
| URL الوصول: | https://doaj.org/article/4579f5f028bd4d3290d6ae324bcb59ab |
| رقم الأكسشن: | edsdoj.4579f5f028bd4d3290d6ae324bcb59ab |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 22967745 |
|---|---|
| DOI: | 10.3389/fmars.2022.1056300 |