## 1 Introduction
Despite yielding better results, YOLOv3 still faces challenges in detecting small objects. A slower but more efficient three-step method uses a modified Mask-RCNN network without a segmentation step [14], as well as another Mask-RCNN network for LP number recognition. Every symbol is found and identified using additional small symbol filtering and clustering based on position. This method has demonstrated promising results [15], with higher precision compared to most other methods. License plates that failed to be detected typically had either very low or very high brightness levels. Currently existing ALPR systems often require significant computer resources, and achieving a balance between high accuracy and speed remains an active topic. With the emergence of the IoT, various solutions have been proposed by developing smaller systems that require less computational power. Using the YOLOv5 light version with IoT devices or YOLOv5 for license plate detection results in faster computations compared to earlier versions of YOLO [16; 17]. Slight improvements are still needed for the proposed solutions, as license plates with poor illumination, captured during nighttime or under adverse weather conditions, are not accurately detected and identified. Convolutional neural network architecture is a crucial factor in achieving speed and quality, and various architecture solutions have been proposed for specific problems. The hourglass-type network is commonly used for human pose estimations, saliency, facial landmark detection, and more [5; 6; 7]. However, hourglass networks are quite large, which results in reduced network speed. [PERSON] et al. presented a system for license plate detection and recognition divided into three subcategories: LP detection, character segmentation, and character detection. License plate detection is accomplished in the following steps: the image (RGB) is converted to an HSV image, small elements are extracted using contrast maximization, a Gaussian blur filter is used to remove details and noise from images, adaptive thresholding is applied to eliminate insignificant regions in the image, contours are found, geometric filtering is applied, and finally, a CNN is used to detect license plates. The CNN is composed of four layers, with two convolutional layers for feature extraction and two fully connected layers [18]. Another license plate detection method for complex backgrounds detects the vehicle region using Fast R-CNN and generates candidate regions. Then, license plates are classified and all non-license plates are removed [19]. Both proposed solutions demonstrated high precision and recall. However, the first step involves identifying the vehicle itself, which requires more computational time and may be too slow for real-time license plate detection. [PERSON] et al. proposed the V-LPDR framework to address the license plate detection and character recognition problem in unconstrained scenarios. The framework uses a novel flow-guided spatiotemporal attention network, which is divided into three modules: detection backbone, flow-guided feature warping, and spatiotemporal attention block. Results show very high accuracy for the proposed solution [20]. The primary drawbacks that persisted included the overall processing time for a frame, which is unsuitable for some real-time applications, and reduced performance with distorted images. [PERSON] et al. proposed a license plate detection method using RetinaFace and MobileNet to predict the license plate from the original image and determine the coordinates of the four key points on the license plate. The output of the detection module is then used as input for the character recognition module [21]. Improvements can still be made to the dataset, as a novel dataset was used for training and it lacked detailed parameters compared to established datasets. This resulted in subpar performance; however, updating the dataset would likely lead to improvement. Additionally, license plates in complex environments were not detected as effectively.