Object and Boundary Detection in Images

Object detection engineering across one- and two-stage detectors, IoU, NMS, boundary extraction and end-to-end latency.

Object detection identifies both what objects are present and where they are located. Depending on the application, outputs may be bounding boxes, contours or masks.

Detector families

Sobel horizontal and vertical derivative kernels applied to an image patch to produce gradient edge magnitude — Sobel edge detection

Two-stage detectors first generate candidate regions and then classify and refine them. One-stage methods such as YOLO predict class and location in a single pass and are widely used in low-latency applications.

Small objects, dense scenes and high input resolution change the trade-off between computation and accuracy.

Overlap and post-processing

IoU measures overlap between predicted and reference boxes. When many candidates correspond to the same object, Non-Maximum Suppression removes duplicates according to confidence and overlap thresholds.

Boundary extraction can use gradient-based techniques such as Canny, active contours or instance-segmentation models.

Real-time performance

FPS alone does not describe end-to-end latency. Decode, resize, memory transfers, model execution, NMS and serialisation all contribute to total delay.

Throughput and single-frame latency should be measured separately, with target hardware and the surrounding data path treated as part of the detector system.

Parent topic: Image and Audio Processing

References

Rafael C. Gonzalez; Richard E. Woods. Digital Image Processing, Fourth Edition. Pearson, 2018.

Richard Szeliski. Computer Vision: Algorithms and Applications, Second Edition. Springer, 2022.