Lightweight Object Detection Model with Data Augmentation for Tiny Pest Detection

With the increasing demand for cost-effective crop pest management solutions, how to achieve effective and efficient automatic pest detection has become the primary research problem. Traditional object detection methods that rely on the quality of handcrafted feature selection are hardly used in pest detection due to the difficulty of designing the features of multiple types of pests. The application of deep learning which presents outstanding performances in object detection tasks faces the following challenges in the field of pest detection. First, the detection difficulties caused by tiny-size pests and protective colouration limit the accuracy of detection. Second, pest detection requires the employment of experts to obtain the annotation of pests for training models, which is costly. Finally, the ability to run on lightweight devices is required due to the limitations of the field environment on networks and equipment. To solve these problems, this paper focuses on a lightweight tiny object detection model, training on limited supervised samples through different data augmentation methods. Different components of object detection models and data augmentation methods are analysed in different sizes of training datasets. Finally, a method based on the Yolo detection model is proposed for pest detection. This pest detection model is evaluated on a real-world aphids data set containing 6k objects. Five sets of data augmentation methods are used on seven sizes of training data sets for analysis. Then the structure of the detection neck of the Yolo model is analysed. Our experimental results show that 54.35% mAP can be achieved by the PAN module and removing the Mosaic data augmentation method for tiny object detection with one hundred samples.