Attention-Aggregated Attribute-Aware Network With Redundancy Reduction Convolution for Video-Based Industrial Smoke Emission Recognition

Abstract

Existing video-based industrial smoke emission recognition methods face the issues of low detection rates and high false alarm rates. An important reason is that they only consider binary category information as supervision information and ignore video attribute information, which provides important supplementary in improving model performances. To solve it, we propose an attention-aggregated attribute-aware network (AANet). First, to effectively guide the model for discriminative feature learning, a video attribute information decoding module is proposed to increase supervision information by designing attribute vector construction and attribute information decoding methods. Second, to learn discriminative feature representations, some attentions are designed to aggregate spatiotemporal and context information based on ConvLSTM, global feature extraction, and cascaded pyramid attention. Final, the redundancy reduction convolution is proposed to reduce redundant channels by channelwise weights considering matrix elements summation and information spatial distribution characterized by information entropy. Extensive experiments show that AANet significantly outperforms existing methods