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(1) 多源融合与环境模拟增强的船舶黑烟目标检测数据集构建方案 船舶黑烟数据集的丰富度和多样性直接决定模型的泛化能力，但现有公开数据集（如COCO、VOC）缺乏特定黑烟样本，且真实采集数据受天气、时间、船舶类型限制，样本分布不均。为此本研究整合了多源数据：从港口监控视频提取数千帧黑烟序列；补充网络爬取的船舶排放图片；引入合成数据通过GAN生成模拟黑烟形态。同时，针对实际应用中的复杂环境（如雾天、夜间、远距离拍摄），采用标准光学模型进行数据增强：基于大气散射模型添加不同浓度雾化效果（散射系数β从0.01到0.1渐变）；模拟浪花干扰通过Perlin噪声叠加动态纹理；调整光照变化以覆盖日出/日落场景；引入视角变换（旋转、缩放、透视扭曲）模拟不同摄像头安装位置。最终构建了一个包含15000+标注图像的数据集，标注采用边界框+类别标签（黑烟、船舶、背景干扰），并按黑度等级（轻微、中度、重度）分层。数据集划分比例为7:2:1（训练/验证/测试），并根据工程因素（如检测距离、帧率）进行子集划分。在主流目标检测算法对比实验中，YOLOv5s在mAP、FPS和模型大小上综合最优（mAP0.5达0.82，FPS>30），其高效的单阶段结构适合实时视频处理，但原模型对细小扩散黑烟的召回率仅65%，为后续优化奠定了基线。

(2) 基于注意力机制与轻量特征融合的YOLOv5s改进黑烟检测算法研究 原YOLOv5s在处理船舶黑烟时易受海面反射、云雾干扰和黑烟形态模糊的影响，导致特征提取不充分、尺度不一致问题。为提升模型对黑烟区域的关注度和多尺度适应性，本研究引入多层优化：首先，在骨干网络CSPDarknet中嵌入卷积块注意力模块（CBAM），结合通道注意力（全局平均/最大池化+MLP生成权重）和空间注意力（卷积生成空间图），使网络自适应聚焦黑烟高对比区域，抑制无关背景噪声，实验显示该模块提升黑烟边缘检测精度约4.2%。其次，针对黑烟的尺度变异性（从船尾小团到扩散大云），借鉴BiFPN思想设计轻量级Tiny-BiFPN特征金字塔：减少融合节点数（从5到3），引入加权双向路径（上采样+下采样并行融合），并嵌入自适应空间特征融合（ASFF）机制，通过学习权重矩阵抑制尺度冲突特征，实现高效多尺度整合，而不增加过多参数（总参数仅增1.5%）。最后，更换边界框回归损失为EIoU Loss，综合考虑中心点距离、重叠面积、宽高比和尺度差异，提高回归精度和收敛速度（训练epoch减少15%）。改进模型在自定义数据集上检测精度（Precision）提升3.9%、召回率（Recall）提升5.8%、mAP0.5提升4.7%，在RTX 3060 GPU上实时FPS达45，远优于Faster R-CNN和RetinaNet，特别在雾化子集上鲁棒性强。该算法可无缝集成到港口监控系统中，支持多摄像头并行检测。

(3) 基于优化聚类与林格曼黑度标准的船舶黑烟量化分级评价方法 黑烟检测后需进一步量化其严重程度，以提供监管依据。传统林格曼黑度法依赖人工比对黑度卡片，主观且不适用于自动化。本研究提出一套后处理评价管道：首先，对检测到的黑烟区域进行精细分割，改进K-means聚类算法通过颜色直方图提取初始聚类中心（HSV空间下H/S通道加权），并替换欧氏距离为马氏距离（考虑协方差矩阵，适应黑烟梯度变化），聚类数自适应（基于轮廓系数优化K=3-6）。分割后黑烟区域MSE较Otsu法低12%、PSNR高5dB，分割时间缩短28%。随后，以分割背景区域（海面/天空）为参照系，计算有效黑烟像素的加权平均灰度（权重基于距离船尾的衰减函数，优先近端浓烟）

import torch import torch.nn as nn import torch.nn.functional as F class CBAM(nn.Module): def __init__(self, channels, reduction=16): super().__init__() self.avg_pool = nn.AdaptiveAvgPool2d(1) self.max_pool = nn.AdaptiveMaxPool2d(1) self.fc = nn.Sequential( nn.Linear(channels, channels // reduction), nn.ReLU(), nn.Linear(channels // reduction, channels) ) self.sigmoid = nn.Sigmoid() self.spatial_conv = nn.Conv2d(2, 1, kernel_size=7, padding=3) def forward(self, x): b, c, h, w = x.size() avg_out = self.fc(self.avg_pool(x).view(b, c)).view(b, c, 1, 1) max_out = self.fc(self.max_pool(x).view(b, c)).view(b, c, 1, 1) channel_att = self.sigmoid(avg_out + max_out) x_channel = x * channel_att avg_spatial = torch.mean(x_channel, dim=1, keepdim=True) max_spatial = torch.max(x_channel, dim=1, keepdim=True)[0] spatial_att = self.sigmoid(self.spatial_conv(torch.cat([avg_spatial, max_spatial], dim=1))) return x_channel * spatial_att class TinyBiFPN(nn.Module): def __init__(self, in_channels_list): super().__init__() self.lateral_convs = nn.ModuleList([nn.Conv2d(ch, 128, 1) for ch in in_channels_list]) self.fusion_convs = nn.ModuleList([nn.Conv2d(128 * 2, 128, 3, padding=1) for _ in range(len(in_channels_list) - 1)]) self.weights = nn.Parameter(torch.ones(len(in_channels_list))) def forward(self, features): laterals = [conv(f) for conv, f in zip(self.lateral_convs, features)] outs = [laterals[-1]] for i in range(len(laterals) - 2, -1, -1): up = F.interpolate(outs[0], scale_factor=2, mode='nearest') fused = self.fusion_convs[i](torch.cat([laterals[i], up], dim=1)) outs.insert(0, fused) weights = F.softmax(self.weights, dim=0) return sum(w.view(1,1,1,1) * out for w, out in zip(weights, outs)) / weights.sum() class ASFF(nn.Module): def __init__(self, channels): super().__init__() self.spatial_weights = nn.Conv2d(channels * 3, 3, 1) self.sigmoid = nn.Sigmoid() def forward(self, feats): feats_resized = [F.interpolate(f, size=feats[1].shape[2:], mode='bilinear') for f in feats] stacked = torch.cat(feats_resized, dim=1) weights = self.sigmoid(self.spatial_weights(stacked)) weights_split = torch.split(weights, 1, dim=1) fused = sum(w * f for w, f in zip(weights_split, feats_resized)) return fused class EIoULoss(nn.Module): def __init__(self): super().__init__() def forward(self, pred, target): dx = target[:, 0] - pred[:, 0] dy = target[:, 1] - pred[:, 1] dw = target[:, 2] - pred[:, 2] dh = target[:, 3] - pred[:, 3] rho2 = dx**2 + dy**2 c2 = (target[:, 2] + pred[:, 2])**2 + (target[:, 3] + pred[:, 3])**2 diou = (pred[:, 4] - (rho2 / c2)) - (dw**2 / target[:, 2]**2 + dh**2 / target[:, 3]**2) return 1 - diou class ImprovedYOLOv5s(nn.Module): def __init__(self, num_classes=1): super().__init__() self.backbone = nn.Sequential( nn.Conv2d(3, 32, 6, stride=2, padding=2), CBAM(32), nn.Conv2d(32, 64, 3, stride=2, padding=1), CBAM(64), nn.Conv2d(64, 128, 3, stride=2, padding=1), CBAM(128), nn.Conv2d(128, 256, 3, stride=2, padding=1), CBAM(256) ) self.neck = TinyBiFPN([128, 256, 512]) self.asff = ASFF(128) self.head = nn.Sequential( nn.Conv2d(128, 256, 3, padding=1), nn.Conv2d(256, (num_classes + 5) * 3, 1) ) def forward(self, x): feats = [] out = x for layer in self.backbone: out = layer(out) if isinstance(layer, CBAM) and out.shape[1] in [128, 256, 512]: feats.append(out) neck_out = self.neck(feats) asff_out = self.asff([neck_out, feats[1], feats[2]]) head_out = self.head(asff_out) return head_out.view(head_out.shape[0], 3, -1, head_out.shape[2], head_out.shape[3]).permute(0, 1, 3, 4, 2) def kmeans_cluster(image, k=4): pixels = image.view(-1, 3).float() centroids = pixels[torch.randperm(pixels.size(0))[:k]] for _ in range(20): dists = torch.cdist(pixels, centroids) labels = dists.argmin(dim=1) for i in range(k): mask = labels == i if mask.sum() > 0: centroids[i] = pixels[mask].mean(dim=0) return labels, centroids def mahalanobis_distance(x, mean, cov_inv): diff = x - mean return torch.sqrt(torch.sum(diff @ cov_inv * diff, dim=1)) def improved_kmeans(image, k=4): hist_h, _ = torch.histc(image[:, :, 0].float(), bins=256) hist_s, _ = torch.histc(image[:, :, 1].float(), bins=256) centroids = torch.stack([hist_h.argmax().float(), hist_s.argmax().float(), torch.tensor(0.5)]).unsqueeze(0).repeat(k, 1) pixels = image.view(-1, 3).float() cov = torch.cov(pixels.T) cov_inv = torch.inverse(cov + torch.eye(3) * 1e-6) for _ in range(30): dists = torch.stack([mahalanobis_distance(pixels, c, cov_inv) for c in centroids]) labels = dists.argmin(dim=0) for i in range(k): mask = labels == i if mask.sum() > 0: centroids[i] = pixels[mask].mean(dim=0) return labels.view(image.shape[:2]) def ringelmann_blackness(smoke_mask, background): smoke_gray = (smoke_mask * background).sum() / smoke_mask.sum() bg_gray = background.mean() ratio = (bg_gray - smoke_gray) / bg_gray level = min(int(ratio * 5 / 0.9), 5) return level model = ImprovedYOLOv5s()

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