Dragon fruit detection in natural orchard environment by integrating lightweight network and attention mechanism - Zhang, Bin; Wang, Rongrong; Zhang, Huiming; Yin, Chenghai; Xia, Yuyang; Fu, Meng; Fu, Wei (Frontiers in Plant Science, 2022)
To realize all-weather detection of dragon fruit in complex orchard environment, an improved lightweight network (Improved YOLOv5s) was proposed based on YOLOv5s in this study. The Ghost Module was introduced in original YOLOv5s to realize the lightweight for model; The coordinates attention mechanism was joined to make the model more accurately locate and identify the dense dragon fruits; The bidirectional feature pyramid network was built to improve the detection effect of dragon fruit at different scales; The SIoU loss function was adopted to improve the convergence speed during model training. The Improved YOLOv5s model was used to detect dragon fruit dataset collected in natural environment. The results showed that the mean average precision (mAP), precision (P) and recall (R) of the model was 97.4%, 96.4% and 95.2%, respectively. The model size, parameters (Params) and floating point operations (FLOPs) were 11.5 MB, 5.2 M and 11.4 G, respectively. Compared with the original YOLOv5s network, the model size, Params and FLOPs of the improved model was reduced by 20.6%, 18.75% and 27.8%, respectively. Meanwhile, the mAP of the improved model was improved by 1.1%. The result proved that the improved model had more lightweight structure and better detection performance. In addition, the average precision (AP) of the Improved YOLOv5s for dragon fruit under the front light, back light, side light, cloudy day and night was 99.5%, 97.3%, 98.5%, 95.5% and 96.1%, respectively. The detection performance met the requirements of all-weather detection of dragon fruit and the improved model had good robustness. This study provides theoretical basis and technical support for fruit monitoring based on unmanned aerial vehicle technology and intelligent picking based on picking robot technology.