A Comparative Analysis of YOLOv10 with Other Object Detection Models: Advantages and Disadvantages to Help You Make the Best Choice

# 1. Overview of Object Detection Models

Object detection models are a type of computer vision technology used to identify and locate objects within images or videos. They are widely used across various fields such as autonomous driving, video surveillance, and medical diagnostics.

An object detection model typically consists of two components: a feature extractor and an object detector. The feature extractor extracts relevant features from the input image or video, while the object detector uses these features to recognize and localize objects.

The performance of an object detection model is mainly determined by the following factors: accuracy, real-time capability, and robustness. Accuracy refers to the model's ability to correctly identify and localize objects, real-time capability refers to the model's inference speed, and robustness refers to the model's ability to maintain performance under various conditions, such as changes in lighting, occlusion, and cluttered backgrounds.

# 2. Theory and Practice of YOLOv10

### 2.1 Network Architecture and Algorithm Principles of YOLOv10

#### 2.1.1 Network Structure of YOLOv10

The network structure of YOLOv10 is based on the CSPDarknet53 backbone network, which consists of a series of convolutional layers, residual blocks, and spatial pyramid pooling (SPP) layers. The CSPDarknet53 backbone network has strong feature extraction capabilities and can effectively extract target features from images.

In YOLOv10, the CSPDarknet53 backbone network is modified to the CSPDarknet53-SPP network. The CSPDarknet53-SPP network adds SPP layers to the CSPDarknet53 backbone network, which can expand the receptive field and enhance the network's ability to detect targets of different scales.

#### 2.1.2 Algorithm Principles of YOLOv10

YOLOv10 adopts a new object detection algorithm, called the YOLOv10 algorithm. The YOLOv10 algorithm mainly includes the following steps:

1. **Image preprocessing:** Adjust the input image to the network input size and perform normalization.
2. **Feature extraction:** Input the preprocessed image into the CSPDarknet53-SPP backbone network for feature extraction, obtaining feature maps.
3. **Feature fusion:** Fuse feature maps of different scales to obtain fused feature maps.
4. **Object detection:** Perform object detection on the fused feature maps to obtain the bounding boxes and class predictions of the objects.

### 2.2 Training and Evaluation of YOLOv10

#### 2.2.1 Training Process of YOLOv10

The training process of YOLOv10 mainly includes the following steps:

1. **Prepare training data:** Collect and annotate target detection datasets and divide the datasets into training sets and validation sets.
2. **Initialize network:** Initialize the weights of the YOLOv10 network, which can use pre-trained weights or random initialization.
3. **Define loss function:** Define the loss function to measure the difference between the network's predictions and the true labels.
4. **Select optimizer:** Select an optimizer to update the network's weights.
5. **Train network:** Input the training set into the network and use the optimizer to update the network's weights to minimize the loss function.
6. **Validate network:** Evaluate the network's performance using the validation set and adjust the network's hyperparameters based on the validation results.

#### 2.2.2 Evaluation Metrics of YOLOv10

The evaluation metrics of YOLOv10 mainly inclu