Segmentation Method for Whole Vehicle Wood Detection Based on Improved YOLACT Instance Segmentation Model

In order to overcome the problems of slow detection speed, low detection accuracy, dense wood stacks and easily obscured and overlooked, a segmentation method based on YOLACT_WOOD is proposed. YOLACT algorithm is proposed to explore the feasibility of a single-stage instance segmentation model for fast and accurate segmentation of whole-truck wood. In this study, based on the original YOLACT model, firstly, the ResNeXt network embedded with the CBAM attention mechanism module is used as the backbone network to imporve the feature extraction capability of the model; secondly, the image input size is increased to improve the detection ability of medium and small diameter class wood; then the CIoU bounding box regression loss function is used to improve the accuracy of bounding box regression; finally, DIoU is combined with Fast-NMS as a boundary box screening algorithm to improve the problem of false and missed detections. In this study, the YOLACT_WOOD algorithm is evaluated using five evaluation metrics: mAP, FPS,
${\mathrm{ IoU}}_{mask}$
, wood true detection rate, and parametric size, and the wood segmentation mask map is fitted and counted using the OpenCV library. The experimental results show that the mAP of this study method is improved by 5.6% compared to the original network, the
${\mathrm{ IoU}}_{mask}$
is improved by 2.6%, the FPS is improved by 14.7 frames/sec compared to the detection speed of the Mask R-CNN model, and the true detection rate of the logs in the test set reaches 96.61%, the false detection rate is 0.23%, and the parametric number of the model is not significantly improved. This result shows that the YOLACT_WOOD model not only ensures the segmentation speed but also improves the segmentation accuracy, solves the problem of false and omission, and the algorithm has strong robustness and generalisation ability.