Abstract
Abstract Detecting the various developmental stages of strawberries in their natural environment is crucial for modern agricultural robots. Existing methods focus on fruit detection but ignore stage classification. Moreover, they usually requiring substantial computational resources and are not suitable for small and low-power embedded platforms. To address this problem, we propose YOLO-VDS, a lightweight model based on YOLOv5s and optimized for embedded platforms. We introduce the Inverse Residual Bottleneck with 3 Convolutions (IRBC3) module to enhance the feature extraction capability and reduce the model computation. Then, we enhance the feature extraction and representation capabilities by introducing Efficient Channel Attention (ECA) in the backbone. Experiments on the Strawberry-DS dataset show that YOLO-VDS performs significantly better than YOLOv5s and other similar algorithms. Compared to YOLOv5s, the accuracy is improved by 5.8%, mAP@ 0.5 by 7.7%, and the number of model parameters is reduced by 24.29%. Deployed on a Jetson TX2 NX, YOLO-VDS reaches 19.2 FPS after TensorRT acceleration, demonstrating its suitability for vision-guided harvesting robots and edge computing applications.
Published Version
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