An Aggressively Pruned CNN Model With Visual Attention for Near Real-Time Wood Defects Detection on Embedded Processors

Abstract

The use of Convolutional Neural Networks (CNN) for the application of wood defects detection has gained significant attention in recent years. In industrial settings, these tasks are typically performed in a strict and consistent environment, making the use of large and complex CNN models unnecessary. Despite this, recent research has continued to focus on the use of such models to achieve increasingly accurate detections. These models require costly machines for inference, making adoption less likely especially for manufacturers in the developing nations. In view of this limitation, this paper proposes a set of strategies to achieve a highly efficient CNN model for fast and accurate wood defects detection based on the YOLOv4-Tiny architecture. The model has been improved with Efficient Channel Attention (ECA) to better select multi-scale features from the backbone network and has been drastically reduced in size through an iterative pruning and recovery process. This results in an 88% reduction in model parameters while retaining accuracy comparable to most state-of-the-art (SOTA) methods. Consequently, the model can perform near real-time inference directly on a general-purpose embedded processor without external hardware accelerators. This research hopes to motivate the development of efficient defect detectors that can run on low-cost embedded devices.