Object Detector for Autonomous Vehicles Based on Faster RCNN

Zirui Wang

• Pytorch implementation of Faster R-CNN based on VGG16.
• Supports Feature Pyramid Network (FPN).
• Supports Deformable Convolution (DCNv1)
• Pretrained model can be found here

1. Introduction

This is a implementation a framework that combines Feature Pyramid Network (FPN) and Deformable Convolution Network (DCNv1) to improve Faster RCNN on object detection tasks.The whole model is implemented on Pytorch and trained on VOC 2007 training set and evaluate on VOC 2007 test set, with 1.1% improvement on mAP@[.5,.95] score and 3.95% improvement on mAP@[0.75:0.95] score, which demonstrates the effectiveness of the model. The model also support for KITTI 2d Object Detection dataset, training on KITTI 2D object detection training set and evaluate on validation set, with a surprisingly 11.96% increase on mAP@[.5,.95] score and a 23.35% increase on mAP@[.75,.95]. m

2. Experimental Results

• Detection results on PASCAL VOC 2007 test set
  • All models were evaluated using COCO-style detection evaluation metrics.

Training dataset	Model	mAP@[.5,.95]	mAP@[.75,.95]
VOC 07	Faster RCNN	69.65	31.14
VOC 07	FPN+ Faster RCNN	69.83	34.02
VOC 07	Deform+ Faster RCNN	69.93	30.85

• Detection results on KITTI 2d Object Detection valication set
  • All models were evaluated using COCO-style detection evaluation metrics.

Training dataset	Model	mAP@[.5,.95]	mAP@[.75,.95]
KITTI 2d	Faster RCNN	71.58	32.40
KITTI 2d	FPN+ Faster RCNN	82.76	56.02
KITTI 2d	Deform+ Faster RCNN	71.73	33.16
KITTI 2d	FPN+ Deform+ Faster RCNN	82.59	56.30

3. Requirements

• numpy
• six
• torch
• torchvision
• tqdm
• cv2
• defaultdict
• itertools
• namedtuple
• skimage
• xml
• pascal_voc_writer
• PIL

4. Usage

4.1 Data preparation

• Download the training, validation, and test data.

# VOC 2007 trainval and test datasets
wget http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtrainval_06-Nov-2007.tar
wget http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtest_06-Nov-2007.tar
# KITTI 2d Object Detection training set and groundtruth labels
wget https://s3.eu-central-1.amazonaws.com/avg-kitti/data_object_image_2.zip
wget https://s3.eu-central-1.amazonaws.com/avg-kitti/data_object_label_2.zip

• Untar files into two separate directories named VOCdevkit and KITTIdevkit

# VOC 2007 trainval and test datasets
mkdir VOCdevkit && cd VOCdevkit
tar xvf VOCtrainval_06-Nov-2007.tar
tar xvf VOCtest_06-Nov-2007.tar

# KITTI 2d Object Detection trainset and labels (following last command)
cd ..
mkdir KITTIdevkit && cd KITTI devkit
unzip data_object_image_2.zip
unzip data_object_label_2.zip

• The KITTI dataset need to reformat to match with the following structure.

dataset
   ├── KITTIdevkit
   │   ├── training
   │       ├── image_2
   │       └── label_2
   └── VOCdevkit
       ├── VOC2007
           ├── Annotations
           ├── ImageSets
           ├── JPEGImages
           ├── SegmentationClass
           └── SegmentationObject

• Convert the KITTI dataset into PASCAL VOC 2007 dataset format using the dataset format convertion tool script

# go back to the main page of the project code
cd ./improved_faster_rcnn
# change directory to find the format convertion script
cd data
# run dataset format convertion script
python kitti2voc.py

• After running the above command, you should have the same dataset structure for KITTI as VOC 2007, and it is now ready to load into the model

dataset
   ├── KITTI2VOC
   │   ├── Annotations
   │   ├── ImageSets
   │   ├── JPEGImages
   └── VOCdevkit
       └── VOC2007
           ├── Annotations
           ├── ImageSets
           ├── JPEGImages
           ├── SegmentationClass
           └── SegmentationObject

4.2 Train models

• You can easily modify the parameters for training in utils/config.py and run the following script for model training

# if you are in local environemnt, run:
python3 ./train.py
# if you are in conda environment, run: 
python ./train.py

4.3 Test models

• You can easily modify the parameters for testing in utils/config.py and run the following script for model training

• You can visualize the testing image by setting visualize=True in configuration file

• The output image should be placed under save_dir/visuals specified in the configuration file

• Ex 1) FPN based on VGG16 (file name: "fpn_vgg16_1.pth")

# if you are in local environemnt, run:
python3 ./test.py
# if you are in conda environment, run: 
python ./test.py

4.4 Example output images

• Below are some of the resulting images from visualization

5. Reference

• simple-faster-rcnn
• fpn.pytorch
• Faster-RCNN-FPN
• Deformable-ConvNets
• PyTorch-Deformable-Convolution-v2