A Novel Feature Fusion Framework for Industrial Automation Single-Multiple Object Detection

Traditional Chinese medicines (TCMs) play an important role in the treatment of many diseases. For industrial production, classical TCMs identification methods suffer from high labor cost and low efficiency. Moreover the complex multi-object combinations of TCMs lead to serious feature confusion problem. In this article, we propose a novel detection network for TCMs called TCMnet. It focuses on the performance degradation caused by the images in different datasets containing different number of objects. First, an innovative multilevel feature fusion framework is proposed, which improves the generalization of the model. Then, a receptive field controlling architecture is established to limit the receptive field for reducing the confusion among multiple objects. Finally, a trainable feature resolution enhancement algorithm is proposed to increase the precision of classifier by enhancing local detail information. In the experiments, we choose 18 classes with 1800 images from our TCMs dataset. The experimental results show that TCMnet proposed in this article is able to mitigate the feature confusion problem in single-multiple object detection. In addition, TCMnet achieves a good accuracy compared with other detectors on single-object and multi-object detection tasks.