Merge pull request #26154 from cabelo:yolov5l

Added and tested yolov5l model. #26154

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [X] The PR is proposed to the proper branch
- [X] There is a reference to the original bug report and related work
- [X] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [X] The feature is well documented and sample code can be built with the project CMake

Below is evidence of the test:

![v5l](https://github.com/user-attachments/assets/f31eff0b-11fc-44de-bdaf-640e67d1d924)
pull/25569/head
Alessandro de Oliveira Faria (A.K.A.CABELO) 2 months ago committed by GitHub
parent 881440c6c6
commit e043d5d9d6
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  1. 17
      samples/dnn/models.yml

@ -90,6 +90,23 @@ yolov8l:
background_label_id: 0 background_label_id: 0
sample: "yolo_detector" sample: "yolo_detector"
# YOLOv5 object detection family from ultralytics (https://github.com/ultralytics/ultralytics)
# Might be used for all YOLOv5n YOLOv5s YOLOv5m YOLOv5l and YOLOv5x
yolov5l:
load_info:
url: "https://github.com/CVHub520/X-AnyLabeling/releases/download/v0.1.0/yolov5l.onnx"
sha1: "9de7e54c524b7fe7577bbd4cdbbdaed53375c8f1"
model: "yolov5l.onnx"
mean: 0.0
scale: 0.00392
width: 640
height: 640
rgb: true
classes: "object_detection_classes_yolo.txt"
background_label_id: 0
sample: "yolo_detector"
# YOLO4 object detection family from Darknet (https://github.com/AlexeyAB/darknet) # YOLO4 object detection family from Darknet (https://github.com/AlexeyAB/darknet)
# YOLO object detection family from Darknet (https://pjreddie.com/darknet/yolo/) # YOLO object detection family from Darknet (https://pjreddie.com/darknet/yolo/)
# Might be used for all YOLOv2, TinyYolov2, YOLOv3, YOLOv4 and TinyYolov4 # Might be used for all YOLOv2, TinyYolov2, YOLOv3, YOLOv4 and TinyYolov4

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