MS-ALN: Multiscale Attention Learning Network for Pest Recognition

Complex backgrounds, occlusions, and non-uniform classes present great challenges to pest recognition in practical applications. In this paper, we propose a multiscale attention learning network to address these problems. This network recursively locates discriminative regions and learns region-based feature representation in four branches. Three newly designed modules, which are target localization, attention detection, and attention removal connect two feature extracting sub-networks in adjacent branches to generate images of different scales. The target localization and attention detection modules locate the discriminative regions to filter out complex backgrounds while the attention removal module randomly removes the discriminative region to encourage the model to tackle occlusions. Thereafter, the parameter-shared classification sub-network follows the feature extracting sub-network in every branch for pest recognition. A decoupled learning strategy is adopted to address the problem of non-uniform classes. We experimented on the widely used IP-102 dataset and achieved state-of-the-art performance.