## 2 Literature Review on Relevant Studies  
### Camera-Based Object Detection  
A convolutional neural network (CNN) represents a subset of the broader deep neural network (DNN) extensively utilized in the realm of AI. It is frequently employed for devising inventive methodologies and algorithms within the OpenCV realm, facilitating complex tasks such as image classification and object detection. This is achieved through the utilization of multiple layers of neurons, simulating the natural visual perception of human beings [10]. Object detection involves a computer vision process aimed at identifying instances of objects belonging to a specific class, categorizing the types of objects, pinpointing their locations, and precisely labeling them within an input image or video. A state-of-the-art object detector essentially pinpoints the position and type of objects within an image [9]. It is a system with three main modules (parts):  
1. Backbone: This module acts as the foundation, extracting salient features from the image and producing a compressed representation through a robust image classifier. Imagine it like a skilled photographer capturing the essence of a scene. 2. Neck: This module acts as the bridge, connecting the backbone and skillfully merging features extracted from different levels of the backbone. Consider it like a data sculptor, gathering and harmonizing different perspectives to create a richer understanding. 3. Head: This module is the decision maker responsible for drawing bounding boxes around objects and classifying their types. Think of it as a detective, analyzing the combined information and identifying what each object is and where it lies. Modern object detectors employ a specialized component known as the head, tasked with both object localization and classification, as aforementioned. Two predominant approaches prevail in head design: single-stage and two-stage architectures. Single-stage detectors, epitomized by YOLO [11], SSD [12], RetinaNet [13], CornerNet [14], and CenterNet [15], prioritize speed and efficiency. They execute both tasks--generating bounding boxes and classifying object types--within a single, unified module. This streamlined approach enables faster inference, making them attractive for real-time applications on computationally constrained platforms like edge devices. However, their focus on efficiency might compromise detection accuracy compared to their two-stage counterparts. In contrast, two-stage detectors, exemplified by the R-CNN family (including Fast R-CNN [16], Faster R-CNN [17], Mask-RCNN [18], Cascade R-CNN [19], and Libra R-CNN [20]), prioritize accuracy over speed. These detectors follow a two-step process: First, a dedicated region proposal network (RPN) generates potential object regions within the image. Subsequently, these proposed regions are forwarded to a separate module for classification and precise bounding box refinement. This  
Figure 1: An overview of the proposed NeXtFusion multi-modal sensor fusion neural network. division of labor leads to superior detection performance but incurs a higher computational cost, limiting their real-time capabilities. When choosing an object detector, striking a balance between accuracy and inference speed is crucial. single-stage detectors like YOLO offer impressive speed, making them suitable for real-time applications like autonomous vehicles. However, for tasks demanding high accuracy, two-stage detectors like Faster R-CNN might be preferred despite their slower performance [21]. Ultimately, the optimal choice hinges on the specific requirements and resource constraints of the application. The neck of an object detector acts as a bridge, meticulously combining features extracted from various depths within the backbone. This intricate network of interconnected pathways, both descending (top-down) and ascending (bottom-up), allows for the seamless integration of multi-scale information.