The project was to detect traffic signs, based on the Megengine framework.

Overview

trafficsign

赛题

旷视AI智慧交通开源赛道,初赛1/177,复赛1/12。
本赛题为复杂场景的交通标志检测,对五种交通标志进行识别。

框架

megengine

算法方案

  • 网络框架

    • atss + resnext101_32x8d
  • 训练阶段

    • 图片尺寸
      最终提交版本输入图片尺寸为(1500,2100)

    • 多尺度训练(最终提交版本未采用)
      起初我们将短边设为(1024, 1056, 1088, 1120, 1152, 1184, 1216, 1248, 1280, 1312, 1344, 1376, 1408),随机选取短边后,长边按比例缩放,并使长边长度小于1800,从而进行多尺度训练,取得了很好的效果。 不过后期的mosaic和mixup在增强时对图片进行了缩放,实则隐含了多尺度训练,且效果优于上述方法,所以我们最终去掉了多尺度训练。

    • 数据增强

      • mosaic增强

        随机选择四张图片,对图片进行随机平移10%,尺度缩放(0.5,2.0),shear 0.1,最后将四张图片进行组合。

      • mixup增强

        随机选取两张图进行叠加,我们最终选用的比例是0.5 * 原图+0.5 * 新图片,同时其进行缩放(0.5,2.0)。

        下图为mosaic+mixup示例图:

        mosaic+mixup

      • 随机水平翻转

        直接对图片进行翻转,会导致第三个类别“arr_l”(左转线)和右转线混淆,故我们添加了class-aware的翻转,遇到有“arr_l”类的图片则不进行翻转。

      • 基于Albumentations库的各种增强(最终提交版本未采用)

        我们尝试了ShiftScaleRotate(验证集+0.5)、CLANE(验证集+1.0)、RandomBrightnessContrast等,但组合起来测试集提点欠佳,所以最后没用。

      • gridmask增强(最终提交版本未采用)

        生成一个和原图相同分辨率的mask(每个grid上全为0或全为1),然后将该mask与原图相乘得到一个图像。提点欠佳,所以没采用。

      • 类别平衡采样(最终提交版本未采用)

        使用类别平衡采样后,效果不是很好,这可能是因为数据集本身没有严重的类别不均衡。下面是我们统计的每个类别在图片中出现的频率。

        红灯 直行线 左转线 禁止行驶 禁止停车
        频率 0.356 0.228 0.201 0.257 0.485
  • 多尺度测试

    • 多尺度测试图片尺寸

      最后提交版本(2100,2700),(2100,2800),(2400,3200),如果继续增加尺度,map还会继续提高。

    • topk—nms

      对上述三个尺度生成的结果先进行nms,再将得到的结果框与剩下所有框进行topk—nms(保留与当前结果框iou大于0.85的topk的框,把这些框的坐标进行融合),参数设置vote_thresh=0.85, k=5。

  • 网络结构

    • 加上增强后,backbone从res50到res101再到resx101有稳定涨点。

    • 我们还在backbone部分尝试了dcn和gcnet,验证集收效甚微,最终没有采用。

模型训练与测试

  • 数据集位置
/path/to/ 
    |->traffic   
    |    |images     
    |    |annotations->|train.json     
    |    |             |val.json     
    |    |             |test.json      
  • 训练测试

在加上增强后,我们训练了36个epoch。

pip3 install --user -r requirements.txt

export PYTHONPATH=your_path/trafficsign:$PYTHONPATH

cd weights && wget https://data.megengine.org.cn/models/weights/atss_resx101_coco_2x_800size_45dot6_b3a91b36.pkl

python3 tools/train.py -n 4 -b 2 -f configs/atss_resx101_final.py -d your_datasetpath -w weights/atss_resx101_coco_2x_800size_45dot6_b3a91b36.pkl

python3 tools/test_final.py -n 4 -se 35 -f configs/atss_resx101_final.py -d your_datasetpath 

(-n 能抢到几张卡就写几吧qaq)

备注

以上提到的所有方法,无论最终是否采用,代码中均有实现。

感谢

https://github.com/MegEngine/Models/tree/master/official/vision/detection

https://github.com/MegEngine/YOLOX

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