An Algorithm Based on Attention Mask for Fine-grained Object Detection

The demands of the application of deep learning for the detection and marking of fine-grained object in the large field of view are increasingly prominent, which can be seen from automatic driving, traffic sign detection, satellite image analysis and so on. Most of the current studies focusing on the fine-grained object detection make an improvement based on the existing object detection algorithms to increase the detection accuracy of fine-grained object. This paper will propose a novel algorithm based on neural network feature constraints, which can realize the detection and marking of fine-grained object via network with the guidance of an attention map. In the procedures of neural network training, the Attention Mask is employed to constrain the loss function of the network and extract feature maps of key areas to alter the weights of key features through self-adaption. In this paper, combined with the needs of nematode detection project, taking nematode detection as an example, the ablation experiments with the employment of UNet network demonstrate that the accuracy rate of fine-grained object detection is increased from zero to around 85% with the additional loss function constraints.