Object detection is an important computer vision task used to detect instances of visual objects of certain classes (for example, humans, animals, cars, or buildings) in digital images such as photos or video frames. The goal is to develop computational models that offer the most critical piece of information required by computer vision applications: “What objects are where?”.

Latest Technological Advances in Computer Vision

Deep learning object detection and tracking are key to a wide range of modern computer vision applications. For example, the identification and detection of objects allow intelligent healthcare monitoring, smart video surveillance, autonomous driving, anomaly detection, robot vision, and so much more. Each AI vision application typically requires a combination of different algorithms that form a flow (pipeline) of numerous processing steps.

How Object Detection Works

Object detection can be achieved by leveraging either conventional image processing techniques or modern deep learning networks.

Image Processing Techniques

These techniques do not need historical data for training. There are various tools available in the market for image processing tasks. The advantage of image processing techniques is that the tasks do not require annotated images. The disadvantage is that these techniques are take into consideration multiple factors, such as complex scenarios (minus a unicolor background), occlusion (partially hidden objects), illumination and shadows, and clutter effect.

Deep Learning Methods

These methods depend on supervised or unsupervised learning. Supervised methods are fundamental in computer vision tasks. Computation power of GPUs limits the performance of these methods. One factor that needs to be is that a huge amount of training data is required. Also, the process of image annotation is labor-intensive and expensive. For example, labeling 500’000 images to train a custom DL object detection algorithm is considered a small dataset. However, many benchmark datasets (MS COCO, Caltech, KITTI, PASCAL VOC, V5) provide labeled data. Today, deep learning object detection is widely accepted by researchers and adopted by computer vision companies to build commercial products.

What is a Single Shot Detector (SSD)?

A Single Shot Detector (SSD) is an advanced object detection algorithm. It stands out for its ability to swiftly and accurately identify objects within images or video frames. What sets SSD apart is its capacity to achieve this in a single pass of a deep neural network, making it remarkably efficient and ideal for real-time applications. SSD achieves this by deploying anchor boxes of different aspect ratios at various positions in feature maps.  This ensures correct identification of objects of all sizes in the image. With its proficiency in detecting various object classes simultaneously, SSD is an excellent tool for tasks that comprise numerous object categories in one image. Its balance between speed and accuracy has made it a widespread choice in applications such as vehicle and pedestrian detection, and broader object detection in fields. For example autonomous driving, surveillance, and robotics.

Key Features of SSD

The following are the key features of SSD:

• Single Shot: Unlike some out-of-date object detection models that leverage a two-stage approach (proposing and classifying regions of interest), SSD performs object detection in a single pass within the network. It directly forecasts the presence of objects and their bounding box coordinates in a single shot, making it faster and more efficient.
• MultiBox: SSD uses a set of default bounding boxes (anchor boxes) of various scales and aspect ratios at different locations in the input image. These default boxes hold prior knowledge about where objects will most likely appear. SSD predicts adjustments to the default boxes to identify objects precisely.
• Multi-Scale Detection: SSD works on different feature maps with different resolutions, enabling it to identify objects of various sizes. Predictions at varying scales capture objects at different levels of granularity.
• Class Scores: SSD predicts the bounding box coordinates and allocates class scores to each default box, indicating the likelihood of an object belonging to a specific category (for example car, bicycle, pedestrian).
• Hard Negative Mining: During training, SSD deploys extensive mining techniques to concentrate on challenging examples, enhancing the model’s accuracy.

Challenges and Limitations of SSDs

The following are the challenges and limitations of SSDs:

• Small Object Detection: One of the primary limitations of SSD is its ability to detect tiny objects. Small objects may pose accuracy challenges in detection. This happens due to anchor boxes not effectively displaying their size and shape within the feature pyramids.
• Complex Backgrounds: Objects in complex or cluttered backgrounds are challenging for identification by SSDs. The model may generate false positives or misclassify objects due to confusing visual information in the surroundings.
• Speed and Accuracy: SSD has excellent speed; however, achieving top-tier accuracy may require trade-offs. In precision-critical applications, we require more accurate object detection methods. If we want an immediate prediction, SSD will have less accuracy.
• Customization Overhead: Fine-tuning SSDs for specific applications can be labor-intensive and resource-consuming.

Conclusion

Object detection is one of the most challenging and fundamental problems in computer vision. As probably the most important computer vision technique, it has received great attention in recent years. This holds true due to the success of deep learning methods such as SSDs that currently dominate the recent state-of-the-art detection methods.