We simply run our neural network on a new image at test time to predict detections. Our base network runs at 45 frames per second with no batch processing on a Titan X GPU and a fast version runs at more than 15
Iii-A1 Analytical image processing techniques 分析图像处理技术已被用于通过提高图像质量来改进目标检测。例如,Yu等[20]引入了一种自适应方法,用于调整医学图像中的对比度,这也可以应用于在低光照条件下改善目标检测。张等[21]提出了一种有效操纵边缘斜率以增强清晰度和降低噪声的方法,这可以在雾天或雪天环境中带来...
并尝试加载和显示 for image_file in image_files: print("Processing:", image_file) # 加载图片 image = cv2.imread(image_file) if image is None: print("Failed to load image:", image_file) continue # 图片增强处理 # 随机亮度调节 image_brightness = np.clip(image * (np.random.uniform(0.5...
[Image-adaptive yolo for object detection in adverse weather conditions]提出了IA-YOLO,它自适应地增强每个图像以提高检测性能。他们提出了一种适用于恶劣天气的可微分图像处理(DIP)模块,并使用小型卷积神经网络(CNN-P)来调整DIP的参数。在IA-YOLO的基础上,[Gdip:...
YOLOv5 Lite在YOLOv5的基础上进行一系列消融实验,使其更轻(Flops更小,内存占用更低,参数更少),更快(加入shuffle channel,yolov5 head进行通道裁剪,在320的input_size至少能在树莓派4B上的推理速度可以达到10+FPS),更易部署(摘除Focus层和4次slice操作,让模型量化精度下降在可接受范围内)。
修改common/src/AclLiteImageProc.cpp和common/src/AclLiteImageProc.h,增加Border()函数 接下来需要在AclLiteImageProc中增加makeBorder的预处理操作 在AclLiteImageProc.h中添加Border()函数声明: ... /** * @brief make border the image to size (width, height) * @param [in]: dest: border image *...
colors=[[random.randint(0,255)for_inrange(3)]for_inrange(len(cls_name))]# 生成随机颜色 model=YOLOv8Detector()# 初始化YOLOv8模型 model.load_model(abs_path("weights/mechanical-yolov8n.pt",path_type="current"))# 加载模型权重 在这里,cls_name定义了要识别的机械零件类型。colors为每种类型生...
directory_path=os.path.dirname(unix_style_path)# 读取YAML文件,保持原有顺序withopen(data_path,'r')asfile:data=yaml.load(file,Loader=yaml.FullLoader)# 修改path项if'path'indata:data['path']=directory_path # 将修改后的数据写回YAML文件withopen(data_path,'w')asfile:yaml.safe_dump(data,file...
(os.sep,'/')# 获取目录路径directory_path = os.path.dirname(unix_style_path)# 读取YAML文件,保持原有顺序withopen(data_path,'r')asfile:data = yaml.load(file, Loader=yaml.FullLoader)# 修改path项if'path'indata:data['path'] = directory_path# 将修改后的数据写回YAML文件withopen(data_path...
[1] RUSSAKOVSKY O,DENG J,SU H,et al.Imagenet large scale visual recognition challenge[J].International Journal of Computer Vision,2015,115(3):211-252. [2] LIN T Y,MAIRE M,BELONGIE S,et al.Microsoft coco:common objects in context[C].European Conference on Computer Vision. Springer,Cham...