yolov8道路瑕疵检测
时间: 2023-12-02 20:42:44 浏览: 67
基于YOLOv8模型的道路瑕疵检测系统可以用于检测和定位道路上的裂纹、检查井、网、裂纹块、网块、坑洼块、坑洼等瑕疵目标。该系统利用深度学习算法实现了图片、视频、摄像头等方式的目标检测,并支持结果可视化和结果导出。以下是该系统的一些功能:
1.训练模型的导入、初始化;
```python
# 导入模型
model = torch.load('model.pth')
# 初始化模型
model.eval()
```
2.检测置信分与检测后处理IOU阈值的调节;
```python
# 设置检测置信分和IOU阈值
conf_thresh = 0.5
nms_thresh = 0.4
```
3.图像的上传、检测、可视化结果展示与检测结果导出;
```python
# 读取图像
img = cv2.imread('test.jpg')
# 图像预处理
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = cv2.resize(img, (416, 416))
img = img.transpose((2, 0, 1))
img = img[np.newaxis, :, :, :]
img = torch.from_numpy(img).float()
# 模型推理
with torch.no_grad():
output = model(img)
# 后处理
output = non_max_suppression(output, conf_thresh=conf_thresh, nms_thresh=nms_thresh)
# 可视化结果
plot_one_box(xyxy, img, color=colors[int(cls)], label=names[int(cls)])
# 结果导出
cv2.imwrite('result.jpg', img)
```
4.视频的上传、检测、可视化结果展示与检测结果导出;
```python
# 读取视频
cap = cv2.VideoCapture('test.mp4')
# 视频预处理
frames = []
while True:
ret, frame = cap.read()
if not ret:
break
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
frame = cv2.resize(frame, (416, 416))
frame = frame.transpose((2, 0, 1))
frame = frame[np.newaxis, :, :, :]
frame = torch.from_numpy(frame).float()
frames.append(frame)
# 模型推理
with torch.no_grad():
outputs = model(frames)
# 后处理
outputs = non_max_suppression(outputs, conf_thresh=conf_thresh, nms_thresh=nms_thresh)
# 可视化结果
for output in outputs:
plot_one_box(xyxy, img, color=colors[int(cls)], label=names[int(cls)])
# 结果导出
out = cv2.VideoWriter('result.mp4', cv2.VideoWriter_fourcc(*'mp4v'), 25, (1280, 720))
for frame in frames:
out.write(frame)
out.release()
```
5.摄像头的图像输入、检测与可视化结果展示;
```python
# 打开摄像头
cap = cv2.VideoCapture(0)
while True:
ret, frame = cap.read()
if not ret:
break
# 图像预处理
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
frame = cv2.resize(frame, (416, 416))
frame = frame.transpose((2, 0, 1))
frame = frame[np.newaxis, :, :, :]
frame = torch.from_numpy(frame).float()
# 模型推理
with torch.no_grad():
output = model(frame)
# 后处理
output = non_max_suppression(output, conf_thresh=conf_thresh, nms_thresh=nms_thresh)
# 可视化结果
plot_one_box(xyxy, img, color=colors[int(cls)], label=names[int(cls)])
cv2.imshow('result', frame)
if cv2.waitKey(1) == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
```