# Iae-km3D a 3D Object Detection Method Based on an Improved KM3D Network
## 1 Introduction  
In recent years, with the development of image processing technology [1] as well as artificial intelligence deep learning technology [2], target detection [3] based on computer vision has been dramatically improved in terms of accuracy and speed. However, in the field related to autonomous driving [4], relying only on the two-dimensional information on the image plane does not effectively obtain the position and structure information of the target in three-dimensional space. Therefore, 3D target detection is crucial for driving self-driving car perception and autonomous systems [5]. Three-dimensional target detection has gradually become a hotspot for research in the field of target detection, and many methods have emerged. Many mature algorithms rely on point cloud information [6], or the fusion of of point cloud and image information. Most existing methods rely heavily on LiDAR data to obtain accurate depth information [7]. However, LiDAR equipment for generating point cloud information is usually expensive and has high maintenance costs [8]. Compared to other sensors, utilizing images obtained from monocular cameras for 3D inspection is an economical and convenient solution. Unlike LiDAR, monocular cameras themselves do not sense spatial depth and cannot provide information about the target position or structure contours in 3D space [9], so using only monocular cameras is more challenging. Since no depth information is available in monocular vision scenarios, most algorithms first acquire 2D information about the target in the image space, then later use neural networks, geometric constraints, or 3D model matching methods to predict the 3D bounding box containing the target [10]. However, in automatic driving scenarios [11], due to the changing road conditions and complex traffic conditions, the targets in front of the vehicle image or video will appear in each other's occlusion, and there is also the problem of changing scale, which brings a significant challenge to the 3D target detection algorithm that relies on monocular RGB images only. At the same time, the automatic driving system needs to detect the surrounding targets in real time in order to make timely judgments and warnings of dangerous situations. Therefore, the algorithm model is required to ensure accuracy while considering the limitations of the computing power of the equipment required for the algorithm's operation, as well as the real-time nature of the algorithm. In recent years, many 3D target detection algorithms have been proposed by domestic and foreign experts and scholars based on monocular vision, which provides sufficient validation for the research prospect in this field. Some of these algorithms require only images as input. At the same time, some methods need to be coupled with additional labeling data or rely on the results produced by other independent network models. The method based on additional data assistance is more common, and Mono3D [12] proposed by [PERSON] et al. in 2016, uses image segmentation technique in 3D prediction, which mainly constructs a 3D candidate frame generation network and assumes that the target to be detected is always located on the ground, and then utilizes the segmentation results, object contours, position a priori, and other intuitive feature information to score the 3D candidate frames projected on the image plane, and ultimately obtains high-quality detection results. As an improvement, the authors of Mono3D proposed 3 DOP in 2017 [13] and added 3D point cloud feature information to score candidate frames, which are estimated from stereo camera pairs. These two algorithms could be more efficient due to the need for many candidate region search operations in 3D space. In addition, some methods use CAD models or object shape information as auxiliary data, e.g., DeepMANTA [14] proposed by [PERSON] et al. in 2017, which uses a coarse-to-fine process to generate accurate 2D object suggestion frames, which are later used to match 3D CAD models from an external labeled dataset.