LDS-YOLO: A lightweight small object detection method for dead trees from shelter forest

The detection and location of dead trees are extremely important for the management and estimating naturalness of the forests, and timely replanting of dead trees can effectively resist natural disasters and maintain the stability of the ecosystem. Dead trees have the characteristics of small targets and inconspicuous detail information, which leads to the problem of difficult identification. In this paper, we propose a novel lightweight architecture for small objection detection based on the YOLO framework, named LDS-YOLO. Specifically, a novel feature extraction module is proposed, it reuses the features from previous layers for the purpose of dense connectivity and reduced dependence on the dataset. Then, for Spatial pyramid pooling (SPP) with the introduction of SoftPool method for retaining detailed information about the object to ensure that small targets are not missed. In the meantime, a depth-wise separable convolution with a small number of parameters is used instead of the traditional convolution to reduce the number of model parameters. We evaluate the proposed method on our self-made dataset based UAV captured images. The experimental results demonstrate that the LDS-YOLO architecture performs well in comparison with the state-of-the-art models, with AP of 89.11% and parameter size of 7.6 MB, and can be used for rapid detection of dead trees in shelter forests, which provides a scientific theoretical basis for forestry management of Three North shelter Forest.

Automated summary

A lightweight small object detection architecture called LDS-YOLO is proposed in this paper, with a novel feature extraction module, SPP with SoftPool method, and depth-wise separable convolution introduced to address the challenge of identifying dead trees efficiently. The experimental results show that the proposed method performs well compared to state-of-the-art models, with a high AP of 89.11% and a small parameter size of 7.6 MB.