# Searching for the Ideal Recipe for Preparing Synthetic Data in the Multi-Object Detection Problem
###### Abstract
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Name** & **Year** & **Img** & **Obj** & **Sim** & **Object of Detection** \\ \hline
[9] & 2023 & ✓ & - & - & Not Specified \\
[6] & 2021 & ✓ & - & - & Road damage and cracks \\
[8] & 2020 & ✓ & - & - & Multi-organ in CT images \\
[7] & 2017 & ✓ & - & - & Groceries \\
[10] & 2023 & - & ✓ & - & Groceries \\
[11] & 2022 & - & ✓ & - & Road Cones \\
[12] & 2019 & - & ✓ & - & Toys and groceries \\
[13] & 2018 & - & ✓ & - & Not specified \\
[14] & 2017 & - & ✓ & - & specific toys in cluttered room \\
[15] & 2015 & - & ✓ & - & Aircraft and drones \\
[16] & 2023 & - & - & ✓ & Vehicles \\
[17] & 2023 & - & - & ✓ & Pedestrians and vehicles \\
[18] & 2023 & - & - & ✓ & Construction yard protection \\
[19] & 2023 & - & - & ✓ & Swimming and drowning people \\
[20] & 2023 & - & - & ✓ & Pedestrians \\
[21] & 2023 & - & - & ✓ & Ants \\
[22] & 2020 & - & - & ✓ & Pedestrians \\
[23] & 2019 & - & - & ✓ & Water closet objects, e.g., toilet, urinal \\
[24] & 2018 & - & - & ✓ & Pedestrians and vehicles \\
[25] & 2018 & - & - & ✓ & Vehicles \\
[26] & 2017 & - & - & ✓ & Pedestrians \\
[27] & 2017 & - & - & ✓ & Groceries \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of different methods of synthetic generation concerning the Img, Obj, Sim and its application for object detection purposes, where Img: images generated without 3D software, Obj: 3D models generated on a flat background, Sim: images rendered using crowd simulations. The second popular category of methods presented in [11; 14] is to use 3D software to render 3D objects on a specific background (Obj: 3D models generated on a flat background). This method works on the assumption that synthetic images do not have to be realistic and ensures that the object is being rendered from multiple angles. Such an approach diversifies the background of an image and provides information about the object from different points of view, which is crucial while detecting more complex shapes. The work in [13] suggests that the best results are obtained when the rendered objects used become background-less, giving the possibility to change the background, as well as to rotate or change their positions. Such an operation should be performed multiple times to ensure filters learn the features of the object, not the background. The authors of [10] use an especially prepared environment with various cameras and visual effects. Objects can be rendered from various camera angles with different light settings. Blur and noise effects are also added to 3D scenes. The last noted group of methods uses a Game Engine for creating a full crowd simulation (Sim: images rendered using simulation; refer to Figure 1), which is proven to be a valuable tool for validating object detection [17], person detection [22], construction site issues [18], and tracking methods [20; 31]. However, it faces limitations in preparing diverse data for training. The challenge lies in the difficulty of achieving a wide range of situations essential for effective training. The complexity lies in addressing the challenge of data diversity in synthetic data generation [13], as certain methods may lead to biased datasets under specific input conditions. Models trained on diverse datasets exhibit enhanced performance when dealing with data beyond the target domain. This is why simulations can only be used to overcome specific problems. One of the possible usages [21] is to render images of walking ants in a randomly generated environment, which is possible because generating forest ground from a top view is a simple task to overcome. Labels are later used to detect, track, and estimate their positions. Another approach [19] uses an ocean simulation to detect people in the water. The simulation uses multiple ship models, varies the weather conditions, and spawns thrash in the sea. The goal of an algorithm is to detect drowning people and to distinguish them from swimming ones.