@inproceedings{8e3ca241abe34a21bbaf2f01f8203681,
title = "TP-YOLO: A Lightweight Attention-Based Architecture for Tiny Pest Detection",
abstract = "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.",
keywords = "Attention mechanism, Cnn, Pest detection, Vision transformers, Yolo",
author = "Yang Di and Phung, \{Son Lam\} and \{Van Den Berg\}, Julian and Jason Clissold and Abdesselam Bouzerdoum",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 30th IEEE International Conference on Image Processing, ICIP 2023 ; Conference date: 08-10-2023 Through 11-10-2023",
year = "2023",
doi = "10.1109/ICIP49359.2023.10222202",
language = "English",
series = "Ieee International Conference On Image Processing Icip",
publisher = "IEEE Computer Society",
pages = "3394--3398",
booktitle = "2023 Ieee International Conference On Image Processing, Icip",
address = "United States",
}