河道水面结冰识别监测系统它能够即时、智能化地监测和识别江河里的水面是否结冰,河道水面结冰识别监测系统进行算法自我学习最新数据的升级。通过加工处理和分析后,马上传送给相关人员,有利于实时监控系统和破冰以便船舶通航,降低人力资源耗费,提高效率。智能化专注于人工智能视频分析技术,使各个领域变成可能。以后将继续加强绿色生态江河管理方法,与时俱进技术性核心理念,提升技术创新设计方案,合理避免绿色生态环境的危害,一同构建绿色当代城市自然环境。
YOLOv5是一种单阶段目标检测算法,该算法在YOLOv4的基础上添加了一些新的改进思路,使其速度与精度都得到了极大的性能提升。主要的改进思路如下所示:
- 输入端:在模型训练阶段,提出了一些改进思路,主要包括Mosaic数据增强、自适应锚框计算、自适应图片缩放;
- 基准网络:融合其它检测算法中的一些新思路,主要包括:Focus结构与CSP结构;
- Neck网络:目标检测网络在BackBone与最后的Head输出层之间往往会插入一些层,Yolov5中添加了FPN+PAN结构;
- Head输出层:输出层的锚框机制与YOLOv4相同,主要改进的是训练时的损失函数GIOU_Loss,以及预测框筛选的DIOU_nms。
河道结冰危害河道水口发电,威协运作安全性河道结冰或者悬浮物非常容易集聚在发电厂进水管的废水格珊前,阻塞废水格珊,,危害发电量水口,导致安全事故关机,不但降低了工程的同时经济效益,并且对工程的安全运行构成威胁。水路里的悬浮物会阻拦船只的出航,悬浮物盘绕飞机螺旋桨会危害船只的可靠,提升船只的检修和维护成本,提升物流成本
# 检测类
class Detect(nn.Module):
stride = None # strides computed during build
export = False # onnx export
def __init__(self, nc=80, anchors=(), ch=()): # detection layer
super(Detect, self).__init__()
self.nc = nc # number of classes
self.no = nc + 5 # number of outputs per anchor
self.nl = len(anchors) # number of detection layers
self.na = len(anchors[0]) // 2 # number of anchors
self.grid = [torch.zeros(1)] * self.nl # init grid
a = torch.tensor(anchors).float().view(self.nl, -1, 2)
self.register_buffer('anchors', a) # shape(nl,na,2)
self.register_buffer('anchor_grid', a.clone().view(self.nl, 1, -1, 1, 1, 2)) # shape(nl,1,na,1,1,2)
self.m = nn.ModuleList(nn.Conv2d(x, self.no * self.na, 1) for x in ch) # output conv
def forward(self, x):
# x = x.copy() # for profiling
z = [] # inference output
self.training |= self.export
for i in range(self.nl):
x[i] = self.m[i](x[i]) # conv
bs, _, ny, nx = x[i].shape # x(bs,255,20,20) to x(bs,3,20,20,85)
x[i] = x[i].view(bs, self.na, self.no, ny, nx).permute(0, 1, 3, 4, 2).contiguous()
if not self.training: # inference
if self.grid[i].shape[2:4] != x[i].shape[2:4]:
self.grid[i] = self._make_grid(nx, ny).to(x[i].device)
y = x[i].sigmoid()
y[..., 0:2] = (y[..., 0:2] * 2. - 0.5 + self.grid[i].to(x[i].device)) * self.stride[i] # xy
y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i] # wh
z.append(y.view(bs, -1, self.no))
return x if self.training else (torch.cat(z, 1), x)
@staticmethod
def _make_grid(nx=20, ny=20):
yv, xv = torch.meshgrid([torch.arange(ny), torch.arange(nx)])
return torch.stack((xv, yv), 2).view((1, 1, ny, nx, 2)).float()
# 根据配置的.yaml文件搭建模型
class Model(nn.Module):
def __init__(self, cfg='yolov5s.yaml', ch=3, nc=None): # model, input channels, number of classes
super(Model, self).__init__()
if isinstance(cfg, dict):
self.yaml = cfg # model dict
else: # is *.yaml
import yaml # for torch hub
self.yaml_file = Path(cfg).name
with open(cfg) as f:
self.yaml = yaml.load(f, Loader=yaml.SafeLoader) # model dict
# Define model
ch = self.yaml['ch'] = self.yaml.get('ch', ch) # input channels
if nc and nc != self.yaml['nc']:
logger.info('Overriding model.yaml nc=%g with nc=%g' % (self.yaml['nc'], nc))
self.yaml['nc'] = nc # override yaml value
self.model, self.save = parse_model(deepcopy(self.yaml), ch=[ch]) # model, savelist
self.names = [str(i) for i in range(self.yaml['nc'])] # default names
# print([x.shape for x in self.forward(torch.zeros(1, ch, 64, 64))])
江河水面结冰和悬浮物的识别对河水和度假区开展实时监测。当监测到水面上面有很多废弃物或者结冰的时候,应该马上报警,并通告管理者及时处理。与此同时,应先报警截屏和视频保存到数据表中,生成汇报并发送给有关管理者。在中后期,能够依据时间范围查看报警纪录、报警截屏视频,进一步提高监控区域的操纵高效率,合理处理大城市水问题,提升地区水管理水平。