# SMYOLO: Lightweight Pedestrian Target Detection Algorithm in Low-Altitude Scenarios
## 1 Introduction  
Unmanned aerial vehicles (UAV) have played an important role in traffic monitoring, route inspection, military, forestry [1, 2, 3, 4], and other fields in recent years. Pedestrian detection is becoming more and more important in applications such as intelligent monitoring, re-identification of people, and autonomous driving. Therefore, drones to detect pedestrians have become an emerging technology. Unlike ordinary target detection, pedestrian detection based on low-altitude drones can provide more comprehensive information and lay the foundation for an in-depth understanding and analysis of pedestrians. However, due to the special viewing angle of low-altitude drones and the low computing power of embedded devices, quickly and accurately detecting pedestrians is still a challenging problem.  
Traditional target detection algorithms lack effective image feature representation methods. The model's generalization ability is insufficient, and it is challenging to complete target detection through general abstract features [5]. The target detection algorithm based on deep learning uses a convolutional neural network (CNN) to extract the target feature richly to complete target detection. Although the current target detection methods based on deep learning have achieved specific achievements, there are still significant challenges in pedestrian detection in unique low-altitude drone scenes. These challenges are mainly: (i) Due to the instability and randomness of the drone shooting camera, it will cause camera shake and aspect ratio changes, which will complicate the background and increase the difficulty of detection to a certain extent; (ii) for pedestrian detection, the drone will distort pedestrians and easily cause false detection when performing overhead shooting; (iii) the scale mismatch between the dataset in the pre-training weight used in the training processand the small target dataset will cause the loss of object feature representation, which will reduce the performance of the detector; (iv) the limited computing power of the drone's airborne platform poses a more significant challenge to the complexity of the detection algorithm; and (v) the latency of high-precision target models is high, affecting how to balance detection accuracy and real-time performance.  
In response to the above problems, this article proposes a new convolutional neural network structure SMYOLO.  
* Aiming at the problem of limited computing power on the drone's airborne platform and high latency of high-precision target models, the SMYOLO target detection model is proposed. The conventional convolution is decomposed and replaced with deep separable convolution and dot convolution, and the activation function is replaced, so as to compress the YOLOv4 model and realize the deployment on the embedded platform.
* Because of the reduced model accuracy after the compressed model and the problem of false pedestrian detection in pedestrian detection, the pre-training dataset and the training dataset are scaled to match, which will improve the feature representation ability of the detector during the training process. SMYOLO has added the BN layer to improve the generalization ability of the detector, thereby improving the accuracy of SMYOLO.  
This algorithm reduces the model's size while ensuring the computing power of the drone's airborne platform, which is very important for the real-time detection of pedestrian scenes. The experimental results and analysis show that compared with the current mainstream methods, the method in this paper increases the detection speed while maintaining the detection accuracy and ensures the detection accuracy of the model while minimizing the volume of the network model as much as possible.