密集堆叠下的高相似度木块横截面检测

快速有效地检测和获取木块横截面信息，是提升木块生产交易效率的关键。由于木块往往被密集堆叠、木块横截面相似度高且边界不明显，给检测木块横截面信息带来了较大的挑战。针对密集堆叠下的高相似度木块横截面检测困难，本文提出了简单高效的Wood R-CNN网络模型，通过改进模型的损失函数和非极大值抑制算法来提升检测精度，简化网络结构和改进特征金字塔网络来保证检测速度。实验证明:该模型可在密集堆叠情况下精确地检测高相似度木块横截面，检测速度较快且鲁棒性良好，可实际运用于木块生产和交易中。

Highly similar wood blocks detection under dense stacking

Quick and effective detection of wood blocks cross section and acquisition of the required information are the key to improving the efficiency of wood block production and transactions. However, since wood blocks are often densely stacked, their cross sections have high similarity, while their boundaries are not obvious; this poses a great challenge in detecting the cross sections and acquiring the required information. Considering the difficulty in detecting the cross section of highly similar wood blocks under dense stacking, a simple and efficient wood R-CNN network model is proposed in this paper. The detection accuracy is increased by improving the model loss function and the non-maximum suppression algorithm, and detection speed is ensured by simplifying the network structure and modifying the feature pyramid network. A series of experiments prove that the algorithm model can precisely detect the cross sections of blocks with high similarity under dense stacking. Moreover, it can guarantee fast detection speed and good robustness and can be actually used in the production and transaction of wood blocks.