ABSTRACT For the sake of enhancing the identification ability of current network and meeting the needs of the high accuracy of distinguishing similar small objects (foliage) in the complex scenes, this paper proposes a modified region-based fully convolutional network which adopts Inception V3 accompanying with residual connection as the main framework. Incorporating deep residual learning module into Inception V3 can not only save the computational cost by factorising convolutions, but also mitigate the vanishing gradients causing the increasing depth of the network. Additionally, this combination can alleviate the degradation problem in the process of extracting features and providing proposals. Experimental results show that the modified approach can identify out different leaves with similar characteristics in one scene, and demonstrate the superiority of our proposed approach over some state-of-the-art deep neural networks, when it comes to recognise foliage in complicated environments.

中文翻译：

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改进的深度神经网络能够识别复杂背景下的树叶

摘要 为了增强当前网络的识别能力，满足复杂场景下相似小物体（树叶）的高精度区分需求，本文提出了一种改进的基于区域的全卷积网络，它采用 Inception V3 和残差连接作为主要框架。将深度残差学习模块纳入 Inception V3 不仅可以通过分解卷积来节省计算成本，还可以减轻导致网络深度增加的消失梯度。此外，这种组合可以缓解提取特征和提供建议过程中的退化问题。实验结果表明，改进后的方法可以在一个场景中识别出具有相似特征的不同叶子，