Weakly supervised attention-based models using activation maps for citrus mite and insect pest classification

Author(s)

Bollis, Edson ; Maia, Helena ; Pedrini, Helio ; Avila, Sandra

• Document type: Journal article
• Source: COMPUTERS AND ELECTRONICS IN AGRICULTURE; v. 195, p. 11-pg., 2022-04-01.

Abstract

Citrus juices and fruits are commodities with great economic potential in the international market, but productivity losses caused by mites and other pests are still far from being a good mark. Despite the integrated pest mechanical aspect, only a few works on automatic classification have handled images with orange mite characteristics, which means tiny and noisy regions of interest. Attention-based models have gained prominence in deep learning research, and, along with weakly supervised learning algorithms, they have improved tasks performed with some label restrictions. In this context, this work proposes an attention-based activation map approach developed to improve the classification of tiny regions called Two-Weighted Activation Mapping, which also produces locations using feature map scores learned from class labels. We apply our method in a two stage network process called Attention-based Multiple Instance Learning Guided by Saliency Maps. We analyze the proposed approach in two challenging datasets, the Citrus Pest Benchmark, captured directly in the field using magnifying glasses, and the Insect Pest, a large pest image benchmark. We evaluate and compare our models with weakly supervised methods, such as Attention-based Deep MIL and WILDCAT. The results show that our classifier is superior to literature methods, surpassing them in all scenarios by at least 16 percentage points. Moreover, our approach infers bounding box locations for salient insects, even training without location labels. (AU)

• FAPESP's process: 20/09838-0 - BI0S - Brazilian Institute of Data Science
• Grantee: João Marcos Travassos Romano
• Support Opportunities: Research Grants - Research Centers in Engineering Program
• FAPESP's process: 13/08293-7 - CCES - Center for Computational Engineering and Sciences
• Grantee: Munir Salomao Skaf
• Support Opportunities: Research Grants - Research, Innovation and Dissemination Centers - RIDC
• FAPESP's process: 17/12646-3 - Déjà vu: feature-space-time coherence from heterogeneous data for media integrity analytics and interpretation of events
• Grantee: Anderson de Rezende Rocha
• Support Opportunities: Research Projects - Thematic Grants