Human brains are powerful and can find objects in images with their visual system. It can perform complicated tasks like identifying objects and finding obstacles with ease. With vast amounts of data, quick GPUs, and better algorithms, the computers are now trained to detect and classify objects in an image accurately.
The objector detector will also count the number of objects in an image and track the location of it precisely while labeling it accurately. For instance, imagine a picture with two dogs and a single person. The object detection tool will scan through the image, classify the objects inside the image, and find examples. We have listed the ten YOLO Alternatives for Real-Time Object Detection.
10 Best YOLO (You Only Look Once) Alternatives for Real-Time Object Detection in 2024
Top 10 Object Detection Tools in 2024
TensorFlow
TensorFlow is one of the YOLO Alternatives for Real-Time Object Detection offering pre-trained tools and models for object detection tasks. It supports different architectures such as Faster R-CNN, EfficientDet, and SSD.
Features
- It has a wide range of pre-trained models made with different architectures.
- Trained on large datasets to detect objects with high accuracy.
- Fine-tine the pre-trained models with their datasets to let the model adapt to your specific object detection tasks.
- User-friendly interface to configure, train, and deploy the object detection models.
Pros
- It is easy to fine-tune the pre-trained models.
- Keep the complexities involved in deep learning at bay and be accessible to developers of different expertise.
Cons
- It is a resource-hungry toolkit
- Performance could have been better.
Pricing: Open source
Faster R-CNN (Region-based Convolutional Neural Network)
Faster R-CNN is one of the best object detection algorithms, and it uses a regional proposal network to generate object bounding boxes. It is highly accurate when region proposals and object classification are used.
Features
- Faster R-CNN will generate region proposals by checking the anchor boxes at different scales and aspect ratios.
- It generates a two-stage detection pipeline where, in the first stage, it creates regional proposals, and in the second phase, it filters and classifies the proposals.
- Extract fixed-size feature maps from the complex feature maps depending on the region proposals.
- Handles the regions of different sizes and locations, making it invariant at various locations and scales.
Pros
- Generate region proposals for different types of objects embedded in an image.
- Have the ability to detect over 80 classes.
Cons
- It takes a lot of time to detect objects.
- The architecture used by this tool has some things that could be improved.
EfficientDet
EfficientDet is one of the best YOLO Alternatives for Real-Time Object Detection that balances accuracy and efficiency. It demonstrates excellent performance on different object detection benchmarks.
Features
- Models developed are highly efficient and scalable to attain balance in the model size.
- Use compound scaling technique to optimize depth, width, and resolution.
- Highly efficient and accurate in detecting objects from images.
- Use multi-scale feature fusion technique to capture and combine features at multiple scales.
Pros
- Boost detection of objects of different sizes and handle objects with varying scale variations.
- Handles challenging object detection scenarios efficiently.
Cons
- It consumes a lot of computational resources to train and fine-tune models.
- To achieve optimal results, you will need well-labeled training data.
RetinaNet
RetinaNet is one of the best object detection models and an alternative to YOLO, which uses a pyramid network and focal loss function. It has developed new techniques to address the critical challenges encountered in object detection.
Features
- They are designed to handle various object detection tasks of multiple sizes.
- It contains a multi-scale feature to let the model detect different objects of different scales.
- Detects small to large objects in the same image, resulting in model accuracy.
- The focal loss function assigns weight to hard-to-classify objects for easy classification.
Pros
- Able to handle imbalance of class, resulting in accuracy.
- Designed to run efficiently and easy to deploy on low-end devices.
Cons
- It encounters a lot of difficulties in finding small objects from images.
- Diverse dataset training is required to attain optimal performance.
Mask R-CNN
Mask R-CNN is one of the best YOLO Alternatives for Real-Time Object Detection, which uses deep learning models to segment pixel-level objects. It allows the model to detect objects and offer precise masks to outline the shape of detected objects.
Features
- Use a regional proposal network to generate the candidate in object regions.
- ROI alignment will address the misalignment problems when quantizing spatial object locations.
- It comes with a mask head to generate object masks that will change the shape of objects.
- The perfect tool to carry out different computer vision tasks.
Pros
- Does instant segmentation offer you pixel-level masks for each object detected?
- Object detection and segmentation accuracy make it robust to create complex scenes.
Cons
- Mask heads will increase the computational resources to make it resource-intensive.
- Need help with real-time performance and use of less powerful hardware.
CenterNet
CenterNet is one of the best YOLO Alternatives for Real-Time Object Detection and is considered the best deep-learning model to predict the center of objects and attributes. It uses a heat maps-based approach to deliver accuracy and efficiency.
Features
- It depends on the heatmap to find the object centers and attributes.
- Predict the attribute size and position of the object.
- Can handle different object types and orientations seamlessly.
- Obtain accurate pixel-level alignment between different features and output masks.
Pros
- It uses a heatmap-based approach to obtain accurate object detection.
- It uses a unique approach to simplify object detection and attain efficient inference.
Cons
- It demands a lot of computational resources compared to other simpler models.
- It contains extensive and diverse datasets but performs differently than expected.
DETR
DETR is the best object detection deep learning algorithm that plays a crucial role in computer vision. It uses transformers’ power to predict object classes and bounding boxes.
Features
- Apply transformers to detect objects seamlessly.
- It uses self-attention to process the images holistically.
- Predicts the position of objects and corresponding classes from the input image.
- Eliminate the need for anchor boxes to streamline the detection process.
Pros
- Use transformer-based architecture to detect objects accurately.
- Have the capability to handle overlapping objects easily.
Cons
- Computationally, it is expensive to attain optimal performance.
- Using large amounts of datasets and smaller datasets leads to suboptimal performance.
Cascade R-CNN
Cascade R-CNN is one of the real-time object detection algorithms and an alternative to YOLO that will improve object detection accuracy with the help of cascading architecture. It is one of the best YOLO Alternatives for Real-Time Object Detection that uses R-CNN networks to find out false negatives and positives.
Features
- Enhance the quality of detection results through its cascading structure.
- Operate in three phases to reduce false positives and false negatives.
- Focus on key aspects of object detection to give accurate results in every phase.
- Generate high-confidence object proposals seamlessly.
Pros
- Give impressive performance on different datasets, setting high standards for detecting object accuracy.
- Detect small objects and keep challenges posed by various objects at bay.
Cons
- Increase the need for computational resources even for simpler models.
- Requires a lot of data that may only be available for some applications.
SSD
SSD, also known as Single Shot multibox detector, uses a deep learning model to detect objects in real time. It is one of the YOLO Alternatives for Real-Time Object Detection that gives high accuracy and efficiency using a single neural network to predict accurate locations of objects.
Features
- SSD object detection anchor boxes that serve the network to predict accurate object locations and sizes.
- Contains feature maps of different resolutions extracted from various stages of the network
- Every feature map is linked to the detection head to predict object class scores.
- Acquire information from different feature maps to detect objects of varying sizes.
Pros
- Detects objects in real-time, making it ideal for use in apps with latency requirements.
- It does object classification and localization at the same time attain higher SSD efficiency.
Cons
- SSD performs great on different object sizes but performs poorly on smaller objects.
- Many datasets are required to obtain accurate results with SSD.
FCOS
FCOS is one of the YOLO Alternatives for Real-Time Object Detection and is a single-stage object detection model that uses critical strides to obtain accuracy and efficiency in the detection of objects. It offers excellent performance in detecting objects from videos and images.
Features
- Extract hierarchical features from different input images.
- Detects objects of different sizes and complexities seamlessly.
- Quickly predict object properties from the convolutional feature maps.
- Easily predict the class, object score, and properties of the object, like object size and center.
Pros
- Achieve great results with different datasets showing accuracy in object detection.
- Process images swiftly without giving up on accuracy.
Cons
- It needs a lot of computational resources to attain optimal performance.
- Improve diverse datasets to improve the model’s performance and use it in different applications.
Different Uses of Object Detection Models
1. Surveillance and security
YOLO Alternatives for Real-Time Object Detection will use the footage captured through the surveillance video to efficiently detect theft, suspicious human activities, people violating traffic rules, and so on.
2. Automobile
Object detection is mandatory for autonomous driving to determine whether the brake, acceleration, and turns are happening correctly. Object detection is a must to determine if there are any cars besides pedestrians, road signs, traffic signs, and other obstacles on the road while driving.
3. Medical
Object detection is widely used in medicine, especially radiology. It will only replace the radiologist’s experience to some extent but will reduce the time spent analyzing the ultrasound, MRI, and CT scans.
What are the YOLO Alternatives for Real-Time Object Detection?
Out of all, TensorFlow is the best as it is an open-source platform with many libraries, resources, and tools. It is used to build and deploy apps related to object detection and supports various languages like Python, C, C++, Javascript, and Java. It accelerates the ML implementation with reduced model size.
Conclusion
Object detection is a critical task in computer vision, and there are many tools or models that we have discussed above that will make it easy to detect objects from images and videos. We have listed the top 10 YOLO Alternatives for Real-Time Object Detection. You can choose the best one that suits your business requirements to use. All the ones listed above are open-source and state-of-the-art models that let you see the magic of object detection.
FAQs – YOLO Alternatives for Real-Time Object Detection
1. How does object detection work?
Object detection will use different machine learning models to analyze the image, get features to find out the regions containing objects, and classify those objects into bounding boxes.
2. What are the critical applications of object detection models or YOLO alternatives?
The YOLO Alternatives for Real-Time Object Detection will be used in video surveillance to retrieve images, detect faces, and perform medical image analysis.
3. What are the most popular YOLO Alternatives for Real-Time Object Detection available?
The popular algorithms include YOLO, SSD, RetainNet, CenterNet, and Mask R-CNN.
4. Can I use object detection in real-time applications?
You can use object detection models in real-time applications, especially since algorithms like SSD and YOLO are designed to give quick inference and briskly process video dynamically.
5. How to train the object detection model?
The object detection model is trained through the labeled dataset with annotated class labels and bounding boxes.