An Improved Lightweight Parameters Network for Strawberry Flowers Detection

Accurate and efficient detection for target crops is crucial to develop intelligent agriculture. A great deal of studies have been devoted to improving the accuracy and efficiency of detection algorithms, but the increasing requirement of computing power makes them particularly difficult to implement on embedded devices. Although some methods have been proposed to accelerate inference by lightening the weights of the algorithms after training, the huge computing power requirements of the algorithms are still a problem. In this paper, an improved lightweight parameters network with lightweight designed backbone and neck by grouped convolution is proposed, which also integrates convolutional (Conv) layers and Batch Normalization (BN) layers to accelerate inference. The experiments in this paper utilize the Strawberry Flower Detection dataset, Tomato dataset, Wind Turbine Detection dataset, and VOC2007 dataset to verify performances of the proposed network. And the results show that the computational cost, the number of parameters, memory footprint and inference time of the improved model are all reduced, while the mean Average Precision(mAP) is increased comparing with the baseline algorithm. Furthermore, the detection performances of the proposed algorithm implemented on Jetson Nano platform indicate it is suitable to be deployed in practical scenarios, especially for embedded platforms with limited computing power.