Pose estimation-dependent identification method for field moth images using deep learning architecture

• Developed pose estimation-dependent method for automated identification of field moth.
• A two-level method is used for on-trap moth image segmentation.
• A deep learning structure is proposed for insect identification.
• Moth identification accuracy improved with pose estimation step.

Due to the varieties of moth poses and cluttered background, traditional methods for automated identification of on-trap moths suffer problems of incomplete feature extraction and misidentification. A novel pose estimation-dependent automated identification method using deep learning architecture is proposed in this paper for on-trap field moth sample images. To deal with cluttered background and uneven illumination, two-level automated moth segmentation was created for separating moth sample images from each trap image. Moth pose was then estimated in terms of either top view or side view. Suitable combinations of texture, colour, shape and local features were extracted for further moth description. Finally, the improved pyramidal stacked de-noising auto-encoder (IpSDAE) architecture was proposed to build a deep neural network for moth identification. The experimental results on 762 field moth samples by 10-fold cross-validation achieved a good identification accuracy of 96.9%, and indicated that the deployment of the proposed pose estimation process is effective for automated moth identification.