发表机构
Khulna University of Engineering & Technology; Bangladesh Army University of Science & Technology（库尔纳工程技术大学; 孟加拉国陆军科技大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对孟加拉国树种识别，提出轻量级BarkNet-Lite网络（2.96M参数）和BarkBD数据集，通过纹理与颜色双路径实现高精度，并验证了决策可解释性。
AI 中文摘要
树种识别支持森林清查和生物多样性监测，但仍依赖于稀缺的分类学专业知识。树皮全年在地面可见，然而树皮识别一直集中于温带植物区系和大型ImageNet预训练骨干网络。我们解决了这两个空白。首先，我们发布了BarkBD，一个针对孟加拉国的树皮数据集：包含四个地区和三种天气条件下20个本地树种的14,258张未裁剪智能手机照片，并采用固定的分层划分。其次，我们提出了BarkNet-Lite，一个从随机初始化训练的2.96M参数网络，将多尺度纹理路径与并行的颜色感知路径配对。在五个随机种子上，它在严格的单图像推理下达到96.64±0.66%的准确率，与在相同协议下微调的九个ImageNet预训练骨干网络相差2.3个百分点以内，并且与其中最小的骨干网络在一个种子级标准差之内，同时迁移到公共基准（在BarkVN-50上为95.86%，在BarkNet 1.0上为92.85%）。通过忠实性和权重随机化检查验证的Grad-CAM确认其决策基于树皮结构而非背景。导出的单精度模型在商用智能手机上对一张照片进行分类耗时15.34毫秒。
英文摘要
Tree species recognition supports forest inventory and biodiversity monitoring but still depends on scarce taxonomic expertise. Bark is visible year-round at ground level, yet bark recognition has concentrated on temperate floras and on large ImageNet-pre-trained backbones. We address both gaps. First, we release BarkBD, a bark dataset for Bangladesh: 14,258 uncropped smartphone photographs of 20 native species across four districts and three weather conditions, with a fixed stratified split. Second, we propose BarkNet-Lite, a 2.96M-parameter network trained from random initialisation, pairing a multi-scale texture pathway with a parallel colour-aware pathway. Over five seeds it reaches 96.64+-0.66%accuracyunderstrict single-image inference, within 2.3 points of nine ImageNet-pre-trained backbones fine-tuned under an identical protocol and within one seed-level standard deviation of the smallest ofthem, andtransfers to public benchmarks (95.86% on BarkVN-50, 92.85% on BarkNet 1.0). Grad-CAM, validated by faithfulness and weight-randomisation checks, confirms its decisions rest on bark structure rather than background. The exported single-precision model classifies one photograph in 15.34ms on a commodity smartphone.
