.png){kind=icon}
返回
Weakly-supervised butterfly detection based on saliency map
.png){kind=icon}
OA
AI
DOI：10.1016/j.patcog.2023.109313.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Given the actual needs for detecting multiple features of butterflies in natural ecosystems, this paper pro-poses a model of weakly-supervised butterfly detection based on a saliency map (WBD-SM) to enhance the accuracy of butterfly detection in the ecological environment as well as to overcome the difficulty of fine annotation. Our proposed model first extracts the features of different scales using the VGG16 with-out the fully connected layers as the backbone network. Next, the saliency maps of butterfly images are extracted using the deep supervision network with shortcut connections (DSS) used for the butterfly tar-get location. The class activation maps of butterfly images are derived via the adversarial complementary learning (ACoL) network for butterfly target recognition. Then, the saliency and class activation maps are post-processed with conditional random fields, thereby obtaining the refined saliency maps of butterfly objects. Finally, the locations of the butterflies are acquired based on the saliency maps. Experimental re-sults on the 20 categories of butterfly dataset collected in this paper indicate that the WBD-SM achieves a higher recognition accuracy than that of the VGG16 under different division ratios. At the same time, when the training set and test set are 8:2, our WBD-SM attains a 95.67% localization accuracy, which is 9.37% and 11.87% higher than the results of the DSS and ACoL, respectively. Compared with three state-of-the-art fully-supervised object detection networks, RefineDet, YOLOv3 and single-shot detection (SSD), the detection performance of our WBD-SM is better than RefineDet, and YOLOv3, and is almost the same as SSD.(c) 2023 Elsevier Ltd. All rights reserved.
Keyword:
Butterfly detection
Saliency map
Class activation map
Weakly -supervised object detection
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
7.6
论文数:
1.3W
被引数:
4.5W
机构
查看更多机构
引用论文
Infrared salient object detection based on global guided lightweight non-local deep features基于全局引导轻量级非局部深度特征的红外显著目标检测
PREAI
分享收藏
The Real-World-Weight Cross-Entropy Loss Function: Modeling the Costs of Mislabeling.png)
IEEE ACCESSOAAI
IEEE ACCESSIF3.6
OAAI分享收藏Overview of deep-learning based methods for salient object detection in videos基于深度学习的视频显著性目标检测方法综述PATTERN RECOGNITIONOAAI
PATTERN RECOGNITIONIF7.6
OAAI分享收藏 没有更多内容