CNN 010

Drawbacks of Anchor-based detectors

It is sensitive to:

• Size
• Aspect Ratio
• Number of Anchor boxes (Fixed)
• To much variation with shape
• Small object
• May not generalize due to pre-defined anchor boxes
• Computation expensive

Anchor-free detectors

• Localize objects without using boxes as proposals

1. Key-point based
2. Center-based

Key-point based

• Locates key object parts in an image
• Detects spatial locations or points unique to an object
• With human body as an example
• Key part of face: nose, eyes, eyebrows, mouth ...
• Key point of human body: joints, elbows, knees ...
• Object is represented using Key-points

Center-based

• Finds positives in the center
• Predicts four distances from the positive to the potential object boundary
  • Top, left, bottom, right
  • {x, y, T, R, B, L}

YOLO

• Yolo V1: 2015
  • darknet backbone
• Yolo V2: 2016
  • Anchor boxes
  • Batch normalization
• Yolo V3: 2018
  • Objectness score
  • improvement for small objects
• Yolo V4/V5: 2020
  • Solid Baseline Model
  • Lightweight and Fast
  • image classification, object detection, and instance segmentation
  • Multiple input processing (Video, Image, Live stream)
  • Optimize weights
  • Developed by Ultralytics (not original author)
• Yolo X/R: 2021
  • Decoupled head
  • First version of Anchor free
  • Improvement efficiency in backbone
• Yolo V6/V7: 2022
  • Faster and more accurate
• Yolo NAS/V8: 2023
  • Anchor free
  • Architectural improvement
  • Strong baseline for realtime object detection
• Yolo V9/V10/V11: 2024
  • Oriented bounding box
  • Strong baseline for oriented object detection
• Yolo V12: 2025
  • Attention mechanism, introduced transformer
  • Little slower
• Yolo 26: 2026
  • Deployment on a small form factor hardware
  • realtime object detection on edge devices
  • Strongest baseline for edge device deployment (realtime and accuracy)
  • Efficient Loss Function

YoloX X

• Anchor-free detector in the Yolo Family
• Decoupled head used
• Label assignment using SimOTA
• Use YoloV3 SPP with DarkNet53 backbone
• Uses advanced augmentation such as Mix-up & Mosaic

• Backbone: Feature extraction
• Neck: Aggregation of multi-scale feature
• Head: Localization and Classification scores

Decoupled head

• Coupled Head: one head gives regression score and classification score (Dog/Cat + Location, BBox)
• Decoupled Head:
  • First head gives Classification score (Dog/Cat)
  • Another head gives Regression score (Location, BBox)

Data Augmentation

• occluded and overlapped objects
• improve model robustness

• four images are combined into one
• crops and resizes the images to create a new training sample

Yolo 26

• Realtime computer vision model
• Detection, Segmentation, Classification, Pose, Tracking, OBB (Oriented Bounding Box)
• Available in Nano, Small, Medium, Large, XLarge
• E2E detection pipeline (NMS-free, Non-Maximum Suppression free)
• Designed for edge AI and fast deployment

Why is it faster

• NMS-free infrerence removes post-processing overhead
• Direct bounding box regression (No DFL, Distribution Focal Loss)
• Lower latency and simpler deploymenet graph
• CPU-optimized architecture
• Up to 43% faster on CPUs than V11

Key Changes

• ProgLoss (Progressive Loss Balancing): improves training stability and convergence
• STAL (Small-Target-Aware Label Assignment): improves small-object detection
• MuSGD optimizer improves convergence speed
• Better speed-accuracy trade-off than many previous YOLO models
• Ideal for robotics, drones, surveillance, and edge devices

Inference pipeline

• Backbone: Efficient Hybrid CNN + Attention
• Neck: PAN-FPN (Multi-scale Feature Fusion)
• Head (Decoupled & Dual Head)
  • One-to-Many Head: Dense supervision (Traning only, many positives)
  • One-to-One Head: Single best match NMS-free inference (Inference & tranining)

Tranining pipeline

Instance Segmentation

• Identifies each pixel of an object instance
• whereas Semantic Segmentation classifies object pixels to specific classes/categories
• Instance Segmentation
  • SegNet
  • DeepMask
  • SharpMask
  • Mask RCNN
• Semantic Segmentation
  • Conditional Random Field (CRF)
  • Fully Convolutional Networks (FCN)
  • U-Net
  • Pyramid scene parsing network (PSPNet)

Application of Instance Segmentation

• Autonomous Driving
• Scene Understanding
• Aerial Image Processing

Mask R-CNN

Mask-Region Convolutional Neural Network

• An addition to the RCNN family, perfoming instance segmentation
• Improved over Faster RCNN
• Full Convolutional Network for predicting mask for each class/object.
• Two stages:
  1. RPN proposes candidiate object bounding boxes
  2. Classify the Candidates, refine bounding boxes, and predict mask.

Limitations of Mask R-CNN

• Computational Complexity: Traning and inference can be computationally intensive, requiring substantial resources (high resolution images or large datasets).
• Small-Object Segmentation: may struggle with accurately segment very small objects due to limited pixel information.
• Data Requirements: Training requires a large amount of annotated data, which can be time-consuming and expensive to acquire.
• Limited Generalization to Unseen Categories: The model's ability to generalize to unseen object categories is limited.

Semantic Segmentation

• input image -> u-net -> output segmentation map

References

• Ge, Z., Liu, S., Wang, F., Li, Z., & Sun, J. (2021). YOLOX: Exceeding YOLO series in 2021. arXiv. https://doi.org/10.48550/arXiv.2107.08430
• Ronneberger, O., Fischer, P., & Brox, T. (2015). U-Net: Convolutional networks for biomedical image segmentation. In N. Navab, J. Hornegger, W. M. Wells, & A. F. Frangi (Eds.), Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015 (Vol. 9351, pp. 234–241). Springer. https://doi.org/10.1007/978-3-319-24574-4_28