| dc.description.abstract | In recent years, machine learning has flourished, such as face recognition, speech recognition, object detection, etc. However, in terms of object detection, it is closely related to people′s daily life. In recent years, automatic cars have been gradually emphasized, and the safety of automatic cars is also a very important issue. Maintaining an appropriate distance from the front car to avoid collision with the preceding car is a key point of automatic cars’ practice. However, the accuracy of vehicle identification is often affected by many factors, the most influential factors are weather conditions, such as, day, night, morning, sun, rain, mist, fog, cloudy. Therefore, we use a convolutional neural network to implement a front-vehicle detection system that can adapt to various weather conditions and remind the driver to avoid accidents.
There are three parts in this paper, the first part is based on Faster-RCNN and inproving it. Faster-RCNN uses VGG16 for extraction features. VGG16 network is relatively large, it needs to occupy more resources, so we use SqueezeNet architecture to improve VGG16 architecture to reduce network size and speed. The second part is replaced ROI pooling layer by ROI align layer to improve the detection results; the third part is calculating the distance from the front car, We use the focal length of the camera and the height of the camera mounted on the car to calculate the distance from the front car.
In the experiment, we use 697 images as training and 233 images as test data. The film contains various weather conditions. The mAP of the object detection system can reach 0.907, and the average test speed of a 640×480 resolution film is 30 frames per second. The number of parameters size is about 7.7 M. | en_US |