Algorithm 1. The YOLOv5 object detection pseudocode

Input: images Output: target detection information   1.  Load pre-trained YOLOv5x model: yolov5x.load_pretrained_model()   2.  Define image preprocessing steps: transform = transforms.Compose ([                         transforms.ToTensor()])   3.  Input images: image = transform(Image.open(image_path)).unsqueeze(0)   4.  Input the image into the YOLOv5x model:      with torch.no_grad():         model.eval()         predictions = model(image)   5.  Analysis of prediction results:   boxes = predictions[..., :4] # Extract the bounding box coordinate information   scores = predictions[..., 4] # Extract the confidence   class_probs = predictions[..., 5:] # Extraction class probability   6.  Non-maximum suppression (NMS):   Selected_boxes = torch.ops.torchvision.Nms (boxes, scores,   iou_threshold = 0.5)   7.  Return the final result:   final_boxes = boxes[selected_boxes]   final_scores = scores[selected_boxes]   final_class_probs = class_probs[selected_boxes]