What is Object Detection?

What is Object Detection?

Object Detection is a computer vision technology that not only identifies what objects are present in an image or video frame but also pinpoints exactly where each object is located. Unlike simple image classification, which assigns a label to an entire image, object detection draws bounding boxes around individual objects and labels each one separately.

For example, given a photograph of a warehouse, an object detection system might identify and locate five pallets, two forklifts, three workers, and one safety hazard, each with a precise position and confidence score.

How Object Detection Works

Modern object detection systems use deep learning architectures specifically designed for this task. The two main approaches are:

One-Stage Detectors

Models like YOLO (You Only Look Once) and SSD (Single Shot Detector) process the entire image in a single pass, predicting object locations and classes simultaneously. These are extremely fast and well-suited for real-time applications like video monitoring.

Two-Stage Detectors

Models like Faster R-CNN first identify regions of interest (areas that likely contain objects) and then classify each region. These tend to be slightly more accurate but slower than one-stage detectors.

The detection pipeline typically involves:

• Feature extraction: The model analyses the image to identify visual patterns at multiple scales
• Region proposal: The system identifies candidate regions that may contain objects
• Classification and localisation: Each candidate region is classified (what is it?) and its bounding box is refined (where exactly is it?)
• Post-processing: Overlapping detections are merged, and low-confidence predictions are filtered out

Business Applications of Object Detection

Inventory Management

Retail and warehouse operations use object detection to count products on shelves or in storage areas, track stock levels in real-time, and identify misplaced or missing items. Cameras mounted in aisles or on drones can automatically generate inventory counts without manual scanning.

Workplace Safety

Construction sites, factories, and warehouses deploy object detection to monitor whether workers are wearing required personal protective equipment (PPE) such as helmets, safety vests, and goggles. The system can alert supervisors in real-time when violations are detected.

Traffic and Vehicle Monitoring

Object detection powers intelligent traffic management systems that count vehicles, detect traffic violations, identify parking availability, and monitor road conditions. This is a key component of smart city initiatives across Southeast Asia.

Retail Analytics

Stores use object detection to understand customer movement patterns, measure dwell time in specific areas, monitor queue lengths, and optimise store layouts. This data helps retailers improve customer experience and increase sales per square metre.

Agriculture

Object detection enables precision agriculture applications such as counting fruit on trees for yield estimation, identifying weeds for targeted spraying, and detecting pests or diseases on individual plants.

Security and Surveillance

Security systems use object detection to identify persons, vehicles, or objects of interest in camera feeds, distinguishing between normal activity and potential security threats. This is far more effective than traditional motion detection, which generates excessive false alarms.

Object Detection in Southeast Asia

Object detection is finding strong adoption across ASEAN markets:

• Smart city deployments: Singapore's extensive camera network uses object detection for traffic management, crowd monitoring, and urban planning. Jakarta, Bangkok, and Kuala Lumpur are following with similar smart city programmes.
• Manufacturing quality: Electronics and automotive manufacturers in Vietnam, Thailand, and Malaysia use object detection to identify specific defects and their locations on products, enabling targeted rework rather than full rejection.
• Port and logistics: Major ports in Singapore, Malaysia, and Indonesia use object detection to track containers, monitor loading operations, and optimise yard management. With Southeast Asia being a critical node in global supply chains, these efficiencies have significant economic impact.
• Plantation management: Palm oil plantations in Malaysia and Indonesia use drone-mounted object detection to count trees, assess health, and estimate yields across vast areas that would be impractical to survey manually.

Real-Time Versus Batch Processing

A critical decision in object detection deployment is whether you need real-time or batch processing:

• Real-time processing (under 100 milliseconds per frame) is needed for safety monitoring, autonomous vehicles, and live surveillance. This typically requires GPU-equipped edge devices or powerful on-premise servers.
• Batch processing (seconds to minutes per image) is sufficient for inventory counting, agricultural surveys, and quality inspection of stored images. This can often be handled cost-effectively using cloud services.

Understanding this distinction helps businesses avoid over-engineering solutions. Many valuable use cases work perfectly well with batch processing at a fraction of the cost of real-time systems.

Accuracy Metrics

Object detection accuracy is measured differently from simple classification:

• Precision: Of all objects the system detected, how many were correct?
• Recall: Of all objects actually present, how many did the system find?
• mAP (mean Average Precision): The standard benchmark metric combining precision and recall across different confidence thresholds

For business applications, the right balance between precision and recall depends on the use case. Safety-critical applications should prioritise recall (catching every safety violation), while inventory counting might prioritise precision (avoiding false counts).

Getting Started

1. Define what objects you need to detect and in what conditions (lighting, angles, distances)
2. Evaluate pre-built solutions first. Cloud APIs like AWS Rekognition, Google Cloud Vision, and Azure Object Detection support common object categories out of the box.
3. For custom objects, collect and label at least 500-1,000 annotated images per object class
4. Choose your processing mode (real-time or batch) based on actual business requirements, not aspirations
5. Start with a controlled pilot in a single location before scaling across multiple sites