# Study on Lightweight Model of Maize Seeding Object Detection Based on YOLOv7
## 1 Introduction
Firstly, two-stage detection models are complex in structure and have more parameters than single-stage models that make models generally unable to be deployed on mobile devices. Secondly, two-stage detection models usually have a slow detection speed, which is limited by candidate box-generation algorithms. They fail to meet the requirements of real-time detection. Due to the issues of two-stage models, single-stage object-detection models are gradually receiving more attention from researchers. In single-stage object-detection models, data are input directly into the backbone network for feature extraction. Compared to two-stage models, the prediction accuracy of single-stage models may be affected due to the generation mode of prediction boxes. However, single-stage models have no feature redundancy extraction and information transmission bottleneck, while the processing speed is faster than two-stage object-detection models. Typical single-stage object-detection models mainly include SSD [16], RetinaNet [17], EfficientDet [18], YOLO [19; 20; 21; 22; 23], etc. Especially with the continuous updates of YOLO models, single-stage object-detection has achieved excellent detection results in the agricultural fields. In their study, [PERSON] and colleagues [24] introduced a novel approach for real-time pest detection using DCNN. Their work demonstrated that this method could achieve higher accuracy with greater efficiency, as well as reduced computational demands. [PERSON] and colleagues [25] proposed a deep-learning scheme for detecting apples in apple farms. Their findings reveal that this method achieved high accuracy, with an accuracy rate of 97%, a recall rate of 99%, and an F0 score of 98.1%. [PERSON] et al. [26] introduced ConvNext and transformer to design the C3 CNTR module. The experimental results showed that on the MAR20 dataset, the mAP increased by 3.5%. [PERSON] et al. [27] proposed a lightweight convolutional neural network. The new network model can achieve a much smaller model size and faster detection speed. [PERSON] et al. [28] proposed a new method based on YOLOv4. The new method has high recognition accuracy, fast recognition speed, and low model complexity. [PERSON] [29] and colleagues proposed a maize weed detection method based on YOLOv4-tiny. The new method introduced an attention mechanism module and a spatial pyramid pooling structure. The experiment results indicate that the mAP value was 86.69% and the detection speed was 57.33 f/s. [PERSON] [30] and their colleagues designed a novel method for detectingplace diseases. The novel method developed a multi-headed DenseNet-based architecture and improved detection accuracy through image fusion. The improved method achieved an average accuracy, recall, precision, and f1 score of 98.17%, 98.17%, 98.16%, and 98.12%, respectively. [PERSON] et al. [31] proposed a convolutional neural network based on inception and residual structure. The experiment results indicate that the overall accuracy is 99.55% in identifying three diseases of corn, potato, and tomato. [PERSON] et al. [32] proposed a corn tassel pose estimation method based on computer vision and directional object detection. The evaluation metrics indicate that the proposed method has a correct estimation rate of 88.56% and 29.57 Giga floating-point operations (GFLOPs). Although YOLO has achieved great success in various domains, there is little research on crop seedling detection. In the field of smart agriculture, research primarily focuses on pest and disease detection, as well as the detection of fruits and vegetables, while seedling detection has received limited attention. Moreover, there is a lack of publicly available datasets specifically designed to train deep-learning models for maize seedling detection. The complex background of maize seedlings in real environments presents a significant challenge for accurate detection using deep-learning models. Furthermore, the large number of parameters in the model contribute to slow inference speed and excessive memory usage.