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In-field cotton detection algorithm based on the dual-path feature extraction
Journal of Electronic Imaging ( IF 1.1 ) Pub Date : 2021-08-01 , DOI: 10.1117/1.jei.30.4.043017 Yang Xu 1 , Yanan Li 1 , Hao Wu 1 , Hongyu Wen 1
Journal of Electronic Imaging ( IF 1.1 ) Pub Date : 2021-08-01 , DOI: 10.1117/1.jei.30.4.043017 Yang Xu 1 , Yanan Li 1 , Hao Wu 1 , Hongyu Wen 1
Affiliation
The complex distribution, mutual occlusion, and scale difference greatly increase the difficulty of cotton detection in the wild. To reduce the omission ratio and raise the detection accuracy of cotton, a dual-path feature extraction (DPFE) cotton detection algorithm is proposed. It consists of a DPFE convolutional neural network, a multi-path feature fusion module, and a multi-scale prediction module. First, the algorithm uses the Darknet network as the main path for feature extraction. At the same time, the double downsampling feature map of the main path is enhanced by a proposed feature enhancement module—spatial pyramid convolution. Then a four-layer convolutional neural structure is designed as the auxiliary path for feature extraction. Finally, multiple feature information is incorporated to locate and recognize cotton with a higher accuracy. In addition, we collected and labeled a cotton dataset with 168 high-resolution images, including 4922 cotton instances for research. The experimental results demonstrate that the DPFE algorithm increases the average detection precision by 9.55% and the recall rate by 13.69%, compared with the traditional algorithm.
中文翻译:
基于双路径特征提取的田间棉花检测算法
复杂的分布、相互遮挡、尺度差异大大增加了野外棉花检测的难度。为减少遗漏率,提高棉花检测精度,提出了一种双路径特征提取(DPFE)棉花检测算法。它由一个DPFE卷积神经网络、一个多路径特征融合模块和一个多尺度预测模块组成。首先,算法使用暗网网络作为特征提取的主要路径。同时,通过提出的特征增强模块——空间金字塔卷积,对主路径的双下采样特征图进行了增强。然后设计了一个四层卷积神经结构作为特征提取的辅助路径。最后,结合多种特征信息,以更高的精度定位和识别棉花。此外,我们收集并标记了一个棉花数据集,其中包含 168 张高分辨率图像,其中包括 4922 个用于研究的棉花实例。实验结果表明,与传统算法相比,DPFE算法平均检测精度提高了9.55%,召回率提高了13.69%。
更新日期:2021-08-15
中文翻译:
基于双路径特征提取的田间棉花检测算法
复杂的分布、相互遮挡、尺度差异大大增加了野外棉花检测的难度。为减少遗漏率,提高棉花检测精度,提出了一种双路径特征提取(DPFE)棉花检测算法。它由一个DPFE卷积神经网络、一个多路径特征融合模块和一个多尺度预测模块组成。首先,算法使用暗网网络作为特征提取的主要路径。同时,通过提出的特征增强模块——空间金字塔卷积,对主路径的双下采样特征图进行了增强。然后设计了一个四层卷积神经结构作为特征提取的辅助路径。最后,结合多种特征信息,以更高的精度定位和识别棉花。此外,我们收集并标记了一个棉花数据集,其中包含 168 张高分辨率图像,其中包括 4922 个用于研究的棉花实例。实验结果表明,与传统算法相比,DPFE算法平均检测精度提高了9.55%,召回率提高了13.69%。