Omni Dimensional Dynamic Convolution News Today : Breaking News, Live Updates & Top Stories | Vimarsana

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 ....

Contextual Transformer , Omni Dimensional Dynamic Convolution , Attention Mechanism , Test Detection , Vision Transformers ,