###### Abstract  
The limited computing resources on edge devices such as Unmanned Aerial Vehicles (UAVs) mean that lightweight object detection algorithms based on convolution neural networks require significant development. However, lightweight models are challenged by small targets with few available features. In this paper, we propose an LC-YOLO model that uses detailed information about small targets in each layer to improve detection performance. The model is improved from the one-stage detector, and contains two optimization modules: Laplace Bottleneck (LB) and Cross-Layer Attention Upsampling (CLAU). The LB module is proposed to enhance shallow features by integrating prior information into the convolutional neural network and maximizing knowledge sharing within the network. CLAU is designed for the pixel-level fusion of deep features and shallow features. Under the combined action of these two modules, the LC-YOLO model achieves better detection performance on the small object detection task. The LC-YOLO model with a parameter quantity of 7.30M achieves an mAP of 94.96% on the remote sensing dataset UCAS-AOD, surpassing the YOLOv5I model with a parameter quantity of 46.61M. The tiny version of LC-YOLO with 1.83M parameters achieves 94.17% mAP, which is close to YOLOv5I. Therefore, the LC-YOLO model can replace many heavyweight networks to complete the small target high-precision detection task under limited computing resources, as in the case of mobile edge-end chips such as UAV onboard chips. small object detection; lightweight network; feature extraction; attention mechanism; feature fusion +
Footnote †: journal: Medical Imaging  
and use them on edge chips and mobile devices. These computing resources are limited, leading to another development direction of convolutional neural networks: lightweight networks [14; 15; 16; 17; 18; 19; 20], that is, those with maximized reasoning speed and reduced use of computing resources, though this comes at the expense of network detection accuracy. Small targets require a more complex network in order to obtain more relevant information in the image and assist in the detection task. However, lightweight networks have fewer parameters, and their feature extraction and feature fusion capability need to be improved, resulting in its limited fitting capacity. Therefore, exploring how to improve the performance of lightweight networks used in small object detection tasks is of great significance for the deployment and practical application of object detection algorithms based on convolutional neural networks on mobile edge chips. In this paper, we propose a lightweight model called LC-YOLO that can be widely used in practical projects and performs better on small object detection tasks. First, we propose Laplace Bottleneck (LB), which takes the classical Laplace operator as prior knowledge and integrates a filter with a fixed weight designed by adjusting the element value into the bottleneck structure through a shortcut. The maximum sharing of previous expertise in the network enhances the feature detail and improves the feature extraction ability of the backbone. Then, to improve the feature fusion ability of the deep network structure, we propose Cross-Layer Attention Upsampling (CLAU). This module takes the shallow feature map as the query tensor and uses the deep feature diagram of the nearest sampling as the key tensor and value tensor. Using the attention mechanism, it obtains the pixel-level weight representing the correlation between the shallow feature and the deep feature, realizes their adaptive fusion, and generates fusion features conducive to small target detection. This can dramatically improve the detection accuracy of the network for small targets. Finally, we obtain the LC-YOLO model by adjusting the function position of the two modules, which uses the extracted features efficiently. When verifying the model's validity on the UCAS-AOD dataset, the mAP was 94.96%.