Saliency Guided Discriminative Learning for Insect Pest Recognition

Recognition of insect pests in the wild plays a key role in crop protection. Large-scale pest recognition in natural scenes is extremely challenging due to significant intra-class variation and small inter-class variation within sub-categories. Existing works typically use state-of-the-art convolutional neural networks (CNNs) to extract global features directly for pest classification, while neglecting the effectiveness of fine-grained features for identifying visually similar pest categories under a specific super-category. In this paper, we propose a saliency guided discriminative learning network (SGDL-Net) to tackle these problems. The proposed SGDL-Net simultaneously mines global features and fine-grained features in a multi-task learning manner. We design two branches with shared parameters for pest datasets with a hierarchical structure: the raw branch and the fine-grained branch. The raw branch is utilized to extract coarse-grained features, i.e., global features, and the fine-grained branch mines fine-grained features through a fine-grained feature mining module (FFMM) as a way to constrain feature learning in the raw branch. In particular, we leverage a salient object location module (SOLM) to locate the salient object in the image and feed it to the fine-grained branch. Finally, through the co-training of the two branches, SGDL-Net is able to learn coarse-grained and fine-grained combined discriminative features via a single CNN. Experimental results show that SGDL-Net achieves state-of-the-art performance on the benchmark dataset IP102 used for insect pest recognition. Meanwhile, ablative studies demonstrate the promise of its application on other hierarchically structured datasets (e.g., CIFAR-100).