# Iae-km3D a 3D Object Detection Method Based on an Improved KM3D Network
## 1 Introduction
MonoGRNet needs to train the network in different modules, first in stages and then end-to-end, which makes the training process time-consuming, and requires sufficient control over the subtasks. GS3D [26], proposed by [PERSON] et al., is based on reliable 2D detection results and first predicts a rough 3D bounding box for the target, which provides a reliable approximation of the target's position, size, and orientation and can be used as a guide for fine-tuning. In an image, the visible surface of an object provides information about the underlying 3D structure, so GS3D projects this 3D bounding box onto the image plane, after which the visible surface features of the object are extracted and fused with the 2D features, which can be used to adjust the rough 3D bounding box to a fine 3D bounding box. M3D-RPN [27], proposed by [PERSON] et al., uses an independent 3D Region Proposal Generation Network to predict multi-class 3D bounding boxes based on the geometric relationship between 2D and 3D perspective views using global convolution and local depth-aware convolution. In order to reduce the tedious 3D parameter estimation, the method further designs a depth-aware convolutional layer for extracting location-specific features to improve the algorithm's performance in understanding the 3D scene. However, M3D-RPN uses an extensive backbone network to improve the results, resulting in many network parameters and high computational complexity. Recently, related work has introduced the idea of key point detection into monocular 3D target detection, which usually uses a more streamlined model structure and improves the running speed of the model. In 2020, [PERSON] et al. proposed SMOKE [28], which designs an end-to-end network that predicts the projection point of the center point of the target's 3D bounding box on the image plane, then predicts the target's local orientation and dimensional information, and later predicts the target's 3D target bounding box from the camera parameters. Since SMOKE detects only one key point, the error is significant. The above method can accomplish 3D target detection based on only RGB images as input, which has broader research significance and application value than models that introduce additional auxiliary data or add additional prediction tasks. However, methods relying solely on RGB images still need to improve in the areas of their detection accuracy at multiple scales and mutual occlusion. Based on the defects of the above networks, this paper proposes an IAE-KM3D network, based on the KM3D network, with the following main contributions:  
1. The Resnet V2 network is introduced, and the residual module is redesigned to improve the training capability of the new residual module with higher generalization. 2. IBN-NET is introduced to carefully integrate Instance Normalization and Batch Normalization as building blocks to continuously improve performance without increasing computational cost. 3. The introduction of [PERSON]'s parameter-free attention mechanism allows the network to focus on more key features and does not increase the network complexity. 4. The Gaussian kernel is improved so that the heat map generated by KM3D is improved from a fixed circle to an ellipse that varies with the width and height of the 3D target, which enhances the algorithm's ability to detect 3D targets. 5. A key point loss function based on the predicted values of key points is proposed to improve the training ratio of the algorithm for complex samples. This paper is organized into four main sections. The Section 1 describes the key issues, challenges of current 3D target detection methods, and illustrates the approaches to address these issues. The Section 2 shows the components of the KM3D network and the components of the improved IAE-KM3D network and describes in detail each improved module of the IAE-KM3D network.