Toward Industrial Densely Packed Object Detection: A Federated Semi-Supervised Learning Approach

Object detection through deep learning techniques plays a pivotal role in various industrial applications, such as defect detection. With industries increasingly recognizing the importance of protecting sensitive data, there is a growing interest in collaborative detector training using federated learning (FL). However, existing FL solutions face challenges in effectively addressing object detection tasks with limited labeled data across practical institutions. This challenge is especially pronounced in scenarios with densely packed objects, where obtaining sufficient labels is time consuming and costly. In this article, we present an innovative federated semi-supervised learning (SSL) framework expressly designed for object detection in densely packed scenes (FSSLOD) to overcome above challenges. To achieve this, our approach leverages a teacher-student network on the client side for local SSL and employs a designed consistency loss to align the output of the teacher network with that of the student network. Furthermore, we present an elastic update mechanism to mitigate the intricate issue of data distribution disparity by preventing the inclusion of inadequately trained knowledge into the shared model. Comprehensive evaluations on two real-world object detection data sets demonstrate that the proposed method significantly enhances object detection performance in densely packed scenes while also ensuring data privacy.