TP-YOLO: A Lightweight Attention-Based Architecture for Tiny Pest Detection

Automatic detection of agricultural pests is a challenging problem that is of great interest in biosecurity and precision agriculture. The detection model must cope well with the dense distribution of small-sized pests in complex backgrounds. This paper proposes a lightweight attention-based network...

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Bibliographic Details
Published in2023 IEEE International Conference on Image Processing (ICIP) pp. 3394 - 3398
Main Authors Di, Yang, Phung, Son Lam, Van Den Berg, Julian, Clissold, Jason, Bouzerdoum, Abdesselam
Format Conference Proceeding
LanguageEnglish
Published IEEE 08.10.2023
Subjects
attention mechanism
CNN
Convolution
Feature extraction
Image edge detection
Object detection
Performance evaluation
Pest detection
Predictive models
Transformers
vision transformers
YOLO
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Summary:Automatic detection of agricultural pests is a challenging problem that is of great interest in biosecurity and precision agriculture. The detection model must cope well with the dense distribution of small-sized pests in complex backgrounds. This paper proposes a lightweight attention-based network, called TP-YOLO, for tiny pest detection. We introduce two attention-based components, namely Contextual Transformer and Omni-Dimensional Dynamic Convolution modules, to enhance feature extraction. The proposed modules are integrated into the YOLOv8 backbone, a state-of-the-art baseline for object detection. This paper also introduces a new benchmark dataset consisting of 1,600 images of Khapra beetles for objective evaluation of pest detection algorithms. Extensive experiments on two datasets indicate that TP-YOLO achieves competitive detection accuracy while having a significantly smaller model size and fast prediction time. We have made the code available to the public at: https://github.com/yangdi-cv/TP-YOLO.
DOI:10.1109/ICIP49359.2023.10222202