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Found 4 papers, 1 papers with code
Aphid Cluster Recognition and Detection in the Wild Using Deep Learning Models
To facilitate the use of machine learning models, we further process the images by cropping them into patches, resulting in a labeled dataset comprising 151, 380 image patches.
On the Real-Time Semantic Segmentation of Aphid Clusters in the Wild
We have collected and labeled a large aphid image dataset in the field, and propose the use of real-time semantic segmentation models to segment clusters of aphids.
A New Dataset and Comparative Study for Aphid Cluster Detection
Aphids are one of the main threats to crops, rural families, and global food security.
Dynamic Label Assignment for Object Detection by Combining Predicted IoUs and Anchor IoUs
For anchor-based detection models, the IoU (Intersection over Union) threshold between the anchors and their corresponding ground truth bounding boxes is the key element since the positive samples and negative samples are divided by the IoU threshold.
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