# Object Detection in Autonomous Driving Scenarios Based on an Improved Faster-RCNN
## 1 Introduction  
In recent years, with changes in market demand and the rapid development of the automotive industry, autonomous driving has become a research hotspot in the automotive field. At present, many Internet companies and automobile companies worldwide have set foot in the field of autonomous driving. Object detection is one of the key algorithms in automatic driving systems, so it is challenging to design an efficient object detection algorithm for this complex scenario. In the field of autonomous driving, the main detected objects are divided into two categories: stationary objects and moving objects. Stationary objects include traffic signs, traffic lights, and obstacles, while moving objects include vehicles, pedestrians, and non-motorized vehicles. The detection of moving objects is particularly important, and there are several difficulties: (1) the object to be detected may have different degrees of occlusion; (2) the detection effect for small objects is not good; and (3) the requirements for detection speed and detection accuracy often cannot be met at the same time. Therefore, whether the above problems can be solved directly affects the safety performance of autonomous driving. Traditional object detection algorithms mostly use sliding windows to extract features of different sizes and regions; then, corresponding classifiers are trained for specific objects, and the trained classifiers are used to classify the extracted features. Since traditional features are designed manually by humans, many of them are designed for specific object detection, and their use has limitations. In many cases, artificial design features cannot describe the essence of an image. Therefore, they are susceptible to various interferencefactors during detection. The model is not robust and cannot be applied in autonomous driving scenarios. The environment faced by autonomous driving is open and complex. To ensure driving safety, the performance requirements of detection algorithms are extremely high. Therefore, most of the current automatic driving schemes use object detection algorithms based on deep learning [1]. These algorithms can be divided into two categories: One type is the two-stage object detection algorithm based on candidate region classification, and the process can generally be divided into two steps. First, region proposals are extracted [2], and then the region proposals are classified and the position coordinates are corrected. This type of algorithm has high accuracy but slow speed. Such algorithms include the region-based convolutional neural network (R-CNN) [3], Fast-RCNN [4], Faster-RCNN [5], Mask-RCNN [6], Cascade-RCNN [7], Libra-RCNN [8], and various improved versions. The other type is the one-stage algorithm based on regression, which transforms the object detection problem into a classification problem. Examples of such algorithms are the single-shot detector (SSD) [9], you only look once (YOLO) [10], YOLO9000 [11], YOLOv3 [12], YOLOv4 [13], and various improved versions. This type of algorithm is fast and has a small model, but it has a very poor detection capability on small objects and is prone to missed and false detections (the \(AP_{s}\) of yolov4 on the COCO dataset is 20.4%, and that of Faster-RCNN is 21.8%). In an autonomous driving system, it is necessary to detect distant objects to make decisions in advance. However, a distant object occupies only a few pixels in an image, which causes great difficulties for the one-stage object detection algorithm. Taking into account the accuracy and detection rate of the model, we selected the Faster-RCNN algorithm as the basis and aimed to improve and optimize the Faster-RCNN algorithm for the problems of occlusion, denseness, and small scale in object detection tasks in autonomous driving scenarios. This paper thus proposes a new algorithm called the Automatic Drive-Faster-RCNN (AD-Faster-RCNN).