What is object detection?

dida

December 3rd 2024

Object detection is a subset of computer vision that involves identifying and localizing objects within images or videos. Unlike general image classification, which assigns a single label to an entire image, object detection is designed to detect and categorize one or multiple objects in an image, and specify their positions. This process is instrumental in a variety of applications ranging from autonomous driving and medical imaging to security and sports analytics.

Object detection combines two primary tasks: object localization and classification. Localization involves pinpointing the location of objects within an image, often represented with bounding boxes, while classification determines the category of the detected object. These combined capabilities allow systems to not only recognize the presence of objects but also to understand their context within an image.

How object detection works

The process of object detection is grounded in the principles of computer vision and digital image processing. An image, when digitized, is transformed into a grid of pixels, which the object detection model analyzes to identify patterns associated with specific objects. The model uses features such as shape, size, and color to detect objects. For example, in self-driving cars, the model recognizes objects like pedestrians or traffic lights by detecting patterns that match the trained data.

The architecture of object detection models typically includes a backbone, neck, and head. The backbone, often derived from pre-trained classification models, extracts features from the image. The neck refines these features and passes them to the head, which generates bounding boxes and assigns classification scores. The backbone extracts feature maps at various resolutions, the neck combines these maps, and the head makes the final object predictions.

Object detection algorithms and architectures

Several algorithms and architectures are used in object detection, with convolutional neural networks (CNNs) playing a significant role. Notable examples include the R-CNN family (Region-based CNN) and the YOLO family (You Only Look Once). R-CNN models generate numerous region proposals and classify each, making them accurate but computationally expensive. YOLO, on the other hand, predicts bounding boxes and classifications in a single network pass, which allows for faster real-time detection but may increase localization errors. Other architectures like SSD and RetinaNet offer simplified yet effective approaches, while DETR combines CNNs with transformers to enhance detection capabilities.

Applications of object detection

Object detection has diverse applications across various industries:

Autonomous Driving: Self-driving cars use object detection to recognize objects such as vehicles and pedestrians, ensuring safe navigation.

Medical Imaging: It assists in identifying diseases by detecting abnormalities in medical scans like X-rays and MRIs.

Security: Real-time detection of weapons or suspicious activities in video surveillance helps in crime prevention.

Object detection at dida

At dida, we are object detection specialists, having worked on many object detection projects in the past. Here you can find a selection:

One example is our project on visual hazard detection, where we detected dangerous objects in front of driving trains.
In another project, we developed an AI solution for the detection of mining pits based on satellite data.
Other AI solutions were developed for detecting agricultural fields or rooftops for solar panel installations.

Recent advances in object detection

Recent research has focused on addressing challenges such as imbalanced datasets and extending object detection to 3D images and video. Techniques like data augmentation help mitigate dataset imbalances, especially in medical imaging. Furthermore, advancements include models that can track objects across video frames despite challenges like motion blur, and the incorporation of transformers and LSTMs to enhance real-time detection capabilities.

Getting started with object detection

To begin with object detection, one must train a model using labeled datasets where objects are annotated with bounding boxes. Tools like Roboflow facilitate this by providing platforms to collect, annotate, and train models efficiently. Whether for industrial applications or research, understanding and deploying object detection models can unlock new possibilities in automation and data analysis.

In summary, object detection empowers systems to recognize and locate multiple objects within images, driving innovation in technology and enhancing capabilities across numerous sectors. Its ability to provide detailed object insights makes it a cornerstone of modern computer vision.