Liu Xiaotang, Xing Zheng, Liu Huanai, Peng Hongxing, Xu Huiming, Yuan Jingqi, Gou Zhiyu
College of Materials and Energy, South China Agricultural University, Guangzhou 510642, China.
College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, China.
Math Biosci Eng. 2022 Sep 15;19(12):13582-13606. doi: 10.3934/mbe.2022634.
Red imported fire ants (RIFA) are an alien invasive pest that can cause serious ecosystem damage. Timely detection, location and elimination of RIFA nests can further control the spread of RIFA. In order to accurately locate the RIFA nests, this paper proposes an improved deep learning method of YOLOv4. The specific methods were as follows: 1) We improved GhostBottleNeck (GBN) and replaced the original CSP block of YOLOv4, so as to compress the network scale and reduce the consumption of computing resources. 2) An Efficient Channel Attention (ECA) mechanism was introduced into GBN to enhance the feature extraction ability of the model. 3) We used Equalized Focal Loss to reduce the loss value of background noise. 4) We increased and improved the upsampling operation of YOLOv4 to enhance the understanding of multi-layer semantic features to the whole network. 5) CutMix was added in the model training process to improve the model's ability to identify occluded objects. The parameters of improved YOLOv4 were greatly reduced, and the abilities to locate and extract edge features were enhanced. Meanwhile, we used an unmanned aerial vehicle (UAV) to collect images of RIFA nests with different heights and scenes, and we made the RIFA nests (RIFAN) airspace dataset. On the RIFAN dataset, through qualitative analysis of the evaluation indicators, mean average precision (MAP) of the improved YOLOv4 model reaches 99.26%, which is 5.9% higher than the original algorithm. Moreover, compared with Faster R-CNN, SSD and other algorithms, improved YOLOv4 has achieved excellent results. Finally, we transplanted the model to the embedded device Raspberry Pi 4B and assembled it on the UAV, using the model's lightweight and high-efficiency features to achieve flexible and fast flight detection of RIFA nests.
红火蚁是一种能对生态系统造成严重破坏的外来入侵害虫。及时检测、定位并消除红火蚁巢穴可进一步控制红火蚁的扩散。为了准确地定位红火蚁巢穴，本文提出了一种改进的YOLOv4深度学习方法。具体方法如下：1）我们改进了GhostBottleNeck（GBN）并替换了YOLOv4原来的CSP模块，以压缩网络规模并减少计算资源消耗。2）在GBN中引入了高效通道注意力（ECA）机制，以增强模型的特征提取能力。3）我们使用均衡焦点损失来降低背景噪声的损失值。4）我们增加并改进了YOLOv4的上采样操作，以增强整个网络对多层语义特征的理解。5）在模型训练过程中添加了CutMix，以提高模型识别遮挡物体的能力。改进后的YOLOv4参数大幅减少，定位和提取边缘特征的能力得到增强。同时，我们使用无人机采集不同高度和场景下的红火蚁巢穴图像，并制作了红火蚁巢穴空域数据集。在该数据集上，通过对评估指标的定性分析，改进后的YOLOv4模型的平均精度均值（MAP）达到99.26%，比原算法高出5.9%。此外，与Faster R-CNN、SSD等算法相比，改进后的YOLOv4取得了优异的效果。最后，我们将模型移植到嵌入式设备树莓派4B上并组装到无人机上，利用模型轻量级和高效的特点实现对红火蚁巢穴灵活快速的飞行检测。
