Adaptive Tiny Object Detection for Improving Pest Detection

In agricultural pest management based on computer vision, numerous species of tiny pests need to be detected in images. However, such tiny detection objects are usually missed when adopting deep detection networks. To improve the detection of tiny pests, this paper presented an adaptive tiny object detection network based on the CenterNet framework. Firstly, a branch with a learnable gating function is integrated into the backbone, and supervised learning is performed on it so that tiny pests' high-resolution feature maps with category and location semantics are exploited, and the learned gating function adaptively controls the combination of such feature maps and the backbone. Moreover, we proposed a size-adaptive weighting method to improve the CenterNet's detection loss function. In training, a higher weight will be assigned to an instance if its size is smaller or its prediction center is farther from the ground truth. Extensive experiments on multiple datasets verify that our two contributions, i.e. the adaptive-gating branch, and the size-adaptive weighting method, are both help to enhance tiny pests' weak feature responses and their discriminations, and further improve the IoU accuracies in detection.