Research on trash detection based on instance segmentation
Sheng Yu, Ye Fei · 2023
An improved hybrid task cascade (HTC) network for a multi-scale trash instance segmentation detection method is proposed to address the problems of ambiguous feature representation and low utilization of feature information in instance segmentation-based trash detection methods. First, multi-scale convolution is introduced in the backbone network based on the HTC network model to improve the backbone network's feature extraction capability for different sizes of trash. Second, improve the feature pyramid network structure by performing feature rescaling on feature channels based on the feature pyramid network, so that the network creates interactions across channels that do not change the spatial information, with the goal of reducing the influence of less important factors while using fewer parameters to achieve the best recognition degree. The experimental results show that the improved HTC network model can achieve robust trash feature extraction, improve detection performance, and significantly improve trash detection accuracy.