A Butterfly Detection Algorithm Based on Transfer Learning and Deformable Convolution Deep Learning
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摘要: 针对自然生态蝴蝶多种特征检测的实际需求，以及生态环境下蝴蝶检测效率低、精度差问题，本文提出了一种基于迁移学习和可变形卷积深度神经网络的蝴蝶检测算法（Transfer learning and deformable convolution deep learning network，TDDNET）.该算法首先使用可变形卷积模型重建ResNet-101卷积层，强化特征提取网络对蝴蝶特征的学习，并以此结合区域建议网络（Region proposal network，RPN）构建二分类蝴蝶检测网络，以下简称DNET-base；然后在DNET-base的模型上，构建RPN网络来指导可变形的敏感位置兴趣区域池化层，以便获得多尺度目标的评分特征图和更准确的位置，再由弱化非极大值抑制（Soft non-maximum suppression，Soft-NMS）精准分类形成TDDNET模型.随后通过模型迁移，将DNET-base训练参数迁移至TDDNET，有效降低数据分布不均造成的训练困难与检测性能差的影响，再由Fine-tuning方式快速训练TDDNET多分类网络，最终实现了对蝴蝶的精确检测.所提算法在854张蝴蝶测试集上对蝴蝶检测结果的mAP0.5为0.9414、mAP0.7为0.9235、检出率DR为0.9082以及分类准确率ACC为0.9370，均高于在同等硬件配置环境下的对比算法.对比实验表明，所提算法对生态照蝴蝶可实现较高精度的检测.Abstract: Aiming at the demand of butterfly multi-features recognition, and the problems of low precision and efficiency of butterfly detection in ecological environment, a butterfly detection with deformable convolution depth neural network based transfer learning is proposed (TDDNET). Firstly, the ResNet-101 convolutional layer is reconstructed by using the deformable convolutional model, which can reinforce the learning of feature extraction network for butterfly features. At the same time, this algorithm is combined with the region proposal network (RPN) to construct a two-classes detection network named DNET-base. Next, on the DNET-base to build TDDNET, the subnetwork RPN is used to guide the deformable sensitive position RoI pooling layer, which can obtain the scores feature map and the multi-scale object location. Then, we use the Soft-nms to obtain better detection results. Finally, the model after DNET-base training is transferred to the TDDNET, and fine-tuning the TDDNET multi-classification parameters. In testing datasets which have 854 images, the butterfly mAP0.5 of the proposed algorithm is 0.9414, mAP0.7 is 0.9235, the detection rate (DR) is 0.9082 and the classification accuracy (ACC) is 0.9370. The experiments demonstrate that the proposed algorithm outperforms the state-of-the-art model in the same hardware environment. The results show that the proposed algorithm can detect butterflies with high accuracy.1) 本文责任编委 金连文
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图 2 本文所提算法TDDNET的原理框架示意图
Fig. 2 Schematic diagram of TDDNET's principle framework proposed in this paper
图 4 $3 \times 3$可变形卷积特征计算过程示例
Fig. 4 An example of deformable convolution feature calculation process ($3 \times 3$)
图 7 构建ResNet单元为可变形ResNet结构
Fig. 7 Construct the ResNet unit as a deformable ResNet structure RoI
图 8 本文所提算法的网络模型与参数说明(TDDNET)
Fig. 8 Network model and parameter description of the algorithm proposed in this paper (TDDNET)
表 1 针对所提算法网络结构自身差异对比
Table 1 Contrast the differences of the network structure of the proposed algorithm
网络结构差异 mAP0.5 mAP0.7 DR ACC TDDNET (Soft-NMS) 0.9415 0.9235 0.9082 0.9370 TDDNET (NMS) 0.9358 0.9208 0.9004 0.9274 DDNET (NMS, 无迁移) 0.9137 0.9009 0.8503 0.9180 TDDNET(无可变形卷积) 0.8827 0.8506 0.8532 0.8728 
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表 2 针对所提算法中在不同层使用可变形卷积模型的差异
Table 2 Aiming at the difference of using deformable convolution network in different layers of the proposed algorithm
可变形卷积网络层 mAP0.5 mAP0.7 DR ACC TDDNET完整框架 0.9415 0.9235 0.9082 0.9370 TDDNET框架(除Res2c) 0.9402 0.9174 0.9004 0.9304 Res5 $(a, b, c)+$ PS RoI 0.9258 0.9076 0.8939 0.9186 PS RoI 0.9106 0.8902 0.8899 0.8960 Res5 $(a, b, c)$ 0.8802 0.8609 0.8693 0.8901
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表 3 所提算法与其他目标检测算法的实验结果
Table 3 Experimental results of the proposed algorithm and other target detection algorithms
对比算法 mAP0.5 mAP0.7 DR ACC Faster R-CNN [12] 0.7879 0.7418 0.8308 0.7845 Faster R-CNN* 0.8207 0.7932 0.8554 0.8144 R-FCN [22] 0.8650 0.8405 0.8650 0.8911 R-FCN* 0.8957 0.8594 0.8747 0.9087 FPN [24] 0.8926 0.8644 0.8994 0.9057 FPN* 0.9288 0.9261 0.8982 0.9206 SSD [25] 0.7794 0.7013 0.8648 0.7564 YOLO-v3 [17] (ResNet50) 0.7787 0.7785 0.8751 0.7956 YOLO-v3 [17] (DarkNet) 0.7889 0.7822 0.8746 0.8050 TDDNET 0.9415 0.9235 0.9082 0.9370
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