Using GMOSTNet for Tree Detection Under Complex Illumination and Morphological Occlusion

Trees are an integral part of the forestry ecosystem. In forestry work, the precise acquisition of tree morphological parameters and attributes is affected by complex illumination and tree morphology. In order to minimize a series of inestimable problems, such as yield reduction, ecological damage, and destruction, caused by inaccurate acquisition of tree location information, this paper proposes a ground tree detection method GMOSTNet. Based on the four types of tree species in the GMOST dataset and Faster R-CNN, it extracted the features of the trees, generate candidate regions, classification, and other operations. By reducing the influence of illumination and occlusion factors during experimentation, more detailed information of the input image was obtained. Meanwhile, regarding false detections caused by inappropriate approximations, the deviation and proximity of the proposal were adjusted. The experimental results showed that the AP value of the four tree species is improved after using GMOSTNet, and the overall accuracy increases from the original 87.25% to 93.25%.