CenterNet-LW-SE net: integrating lightweight CenterNet and channel attention mechanism for the detection of Camellia oleifera fruits

Camellia oleifera is a typical dense and small economic oil fruit. Its traditional harvesting method relies on manual labor, which is inefficient and costly. It is of significance to develop automatic harvesting machinery, which relies on rapid and accurate detection of Camellia oleifera in natural and complex environment. In this study, a novel model named CenterNet-LW-SE was proposed. Based on the structure of CenterNet, the lightweight Deep Layer Aggregation was used as the feature extraction backbone and the Squeeze-and-Excitation module was embedded to enhance the ability of feature extracting. A total of 4700 images were used to train and test the algorithms. The precision, recall, \({AP}_{75}\) ${\mathit{AP}}_{75}$ , \({F}_{1}\) $F_1$ , model size and detection speed of CenterNet-LW-SE were 94.70%, 87.90%, 90.00%, 91.17%, 65.65 MB and 5.03 fps, respectively. Compared with the original CenterNet its \({AP}_{75}\) ${\mathit{AP}}_{75}$ was improved by 2.1%, its model size was reduced by 16.69%, and its average detection time on the development board was reduced by 30.40%. Experimental results showed that for severely occluded, shadowy and blurred fruits, CenterNet-LW-SE had better robustness and portability than CenterNet-DLA34, YOLOv4-tiny and RetinaNet. This study can provide a reference for the research and development of mechanical harvesting equipment for Camellia oleifera and the decision-making of Camellia oleifera forest.