Research on Volume Measurement of Logs Based on Embedded Application

Abstract

Spurred by the worldwide concern for forest protection and the increased log sales, most countries have standardized log volume calculations to avoid excessive timber and protect buyers. However, log volume is currently manually measured, suffering from high labor costs, low measurement progress, and imposing significant measurement errors. Thus, automatically obtaining the volumetric data of logs is a convenient and quick solution. Therefore, this work proposes a Mask Region Convolutional Neural Networkbased (R-CNN) algorithm for Logs volume measurement, named Wood Mask (WM) R-CNN. Specifically, we employ the Res2Net structure as the backbone to obtain receptive fields that exceed the input feature size, thus improving our model's multi-scale information fusion ability. Additionally, WM R-CNN relies on the Path Aggregation Feature Pyramid Network's (PAFPN's) path enhancement structure, shortening the low-level feature map's propagation path and improving the wood contour segmentation accuracy. Extensive experiments on the Vehicle-mounted Dense Logs (VMDL) dataset demonstrate that WM R-CNN affords a highly appealing segmentation accuracy for small, medium, and large wood, improving the corresponding mAP indicators against current methods by 2.0%, 1.2%, and 4.4%, respectively. Furthermore, a quantitative method based on TensorRT compresses the proposed model to deploy the WM R-CNN to mobile embedded devices. However, to compensate for the quantization loss, we introduce the expansion convolution operation method to manipulate the mask map and control the volume calculation error of all logs on a vehicle within 1%. The experiments reveal that the proposed method offers an appealing performance, verifying the algorithm's effectiveness and implementation ability on mobile terminals.

A Lightweight YOLO Object Detection Algorithm Based on Bidirectional Multi‐Scale Feature Enhancement

“…On the VOC 2012 dataset, compared with other algorithms, our algorithm uses a lightweight Ghost module, which may result in a certain loss of detection accuracy, but it has advantages in terms of algorithm parameter quantity and detection speed. After the fusion of the improved BFNet and NCA modules in this manuscript, the detection accuracy has improved slightly, mAP@0.5 It reached 79.0%, which is very close to the literature [35] . Compared with Liu et al [ 33] (2022) VOC 2007 SSD 300 × 300 76.5 --71.…”