# Improved YOLOv5: Efficient Object Detection Using Drone Images under Various Conditions
## 1 Introduction  
Recently, drones have been a field that is developing a lot, and they are likely to be combined into various fields in the future to create high value. Especially, low-budget drone photography technology can boost the local economy or help scientists research cultural heritage areas on the coast [1, 2]. In this paper, we study the performance improvement of object detection model using drone photography.  
There are also many cases of searching for object using drones at accident or disaster sites. However, it is confusing to detect missing persons or objects in a situation where visibility is not secured due to heavy rain and snow.  
On the 10 th of 2021, at least 40 tornadoes occurred in six weeks, including Kentucky, Arkansas, Illinois, Missouri, Tennessee, and Mississippi, confirming that at least 84 people were killed [3]. In this case, the number of missing persons will be much higher than the death. In this situation of lifesaving, a detection technique using a drone [4]; could be a solution. Drones and UAVs (unmanned aerial vehicles) have done many missions recently.  
For example, be studied in fields such as automatic license plate recognition [5]; detection of the diseased plant [6]; traffic light detector for self-driving vehicles [7, 8]; for violent individual identification [9]; and detector for ship detection in SAR Images [10]. Searching for missing objects in a disaster situation or used in operational missions in war situations, and it is necessary in a situation where medical staff can quickly find injured people at the accident site [11, 12, 13].  
However, detection using such drone is greatly affected by surrounding situations [14]. To solve this problem, object detection using drones has been researched and developed [15], but related research is lacking a lot.  
Additionally, it can be used in numerous situations as well as the above-mentioned situations. In the future, object detection using drones will be further developed and necessary in various situations. This paper discusses how to detect well in environment that is confusing to recognize objects to solve these problems. We were able to efficiently improve the performance of the model through Conv layer modification, the main layer of the original YOLOv5. In this work, we demonstrate the association of activation function with mAP (0.5) and loss function.  
In this paper, we can summarize our main contributions as follows:  
* Firstly, we improved the performance of model that can detect object under various environmental and weather conditions, such as Clear, Cloudy, Rainy, Snowy day, Evening, Night, Low altitude, and High altitude.
* Secondly, the Precision and mAP (0.5) were increased by modifying the Conv layer, the main layer of the Original YOLOv5 model. We replaced the SiLU activation function of the Conv layer with the ELU activation function. We applied the replaced ConvELU layer to the original C3, SPPF, and Conv layer of the Backbone and head part, and we used CIoU in two models: Original YOLOv5 and YOLOv5_Ours to find association with ELU activation function. As a result, we were able to reduce the convergence speed of loss function at the training process.