Locally Grouped and Scale-Guided Attention for Dense Pest Counting

TL;DR

This paper proposes a novel attention-based framework for dense pest counting that effectively handles occlusion, pose variation, and similar appearances, outperforming existing models significantly.

Contribution

It introduces a locally grouped and scale-guided attention mechanism integrated into a multiscale CenterNet framework for the first time in dense pest counting.

Findings

Outperforms state-of-the-art pest counting models by a large margin.

Effectively handles occlusion and pose variation in dense pest scenarios.

Enhances discriminative feature learning through local grouping and attention mechanisms.

Abstract

This study introduces a new dense pest counting problem to predict densely distributed pests captured by digital traps. Unlike traditional detection-based counting models for sparsely distributed objects, trap-based pest counting must deal with dense pest distributions that pose challenges such as severe occlusion, wide pose variation, and similar appearances in colors and textures. To address these problems, it is essential to incorporate the local attention mechanism, which identifies locally important and unimportant areas to learn locally grouped features, thereby enhancing discriminative performance. Accordingly, this study presents a novel design that integrates locally grouped and scale-guided attention into a multiscale CenterNet framework. To group local features with similar attributes, a straightforward method is introduced using the heatmap predicted by the first hourglass containing pest centroid information, which eliminates the need for complex clustering models. To enhance attentiveness, the pixel attention module transforms the heatmap into a learnable map. Subsequently, scale-guided attention is deployed to make the object and background features more discriminative, achieving multiscale feature fusion. Through experiments, the proposed model is verified to enhance object features based on local grouping and discriminative feature attention learning. Additionally, the proposed model is highly effective in overcoming occlusion and pose variation problems, making it more suitable for dense pest counting. In particular, the proposed model outperforms state-of-the-art models by a large margin, with a remarkable contribution to dense pest counting.