A novel lightweight model HGCA-YOLO: Application to recognition of invisible spears for white asparagus robotic harvesting

Abstract

Timely harvesting white asparagus depend on the detection of two forms: the emerging unearthed spear tips and the soil leaks raised by earthed spears. Accurate detection of the small spear tips and the invisible spears in the complex backgrounds of field ridges remains a challenge, especially the soil leaks with pattern similarity to the drought-induced cracks. In this paper, a novel lightweight model named HGCA-YOLO (Hyperparameter evolution-Ghost module-Coordinate Attention mechanism You Only Look Once algorithm) is proposed for accurate detection of two forms of targets. Firstly, the baseline network is determined by adopting the hyperparameter evolution to converge the network faster and obtain better parameters for spear detection. Then, the Ghost module and coordinate attention mechanism are introduced in the baseline network to decrease the complexity of the model as well as to enhance the sensitivity to the target location. In addition, the TTA (Test Time Augmentation) is introduced to the network inference to handle the targets in strongly varying environment. Finally, a dataset covered spear tips and soil leaks acquired from natural ridge is constructed, and the detection test and field experiments are conducted. The experimental results show that the accuracy of the proposed method achieved mAP 0.952 and mF1 0.924. Compared to the baseline network, this method reduced the parameters, GFLOPs and model size by 46.2%, 48.4% and 44.7%. In particular, the success rate of detection achieved 87% in the field test. This lightweight model effectively extracted the features of the cracking pattern of invisible asparagus and the small spear tips, which improved the accuracy of spear detection for selective robotic harvesting of white asparagus.