Object Detection in Photography Using Deep Learning

Object detection in photography has developed fast due to deep learning and has changed the manner in which visual content is photographed, arranged, and understood. This paper is a detailed examination of the current detection systems and how they can apply to the photographic process. Starting with the description of classical approaches like HOG, Haar cascades, and SVM-based networks, the paper compares the drawbacks of the mentioned methods with the advancement of CNN-based frameworks. R-CNN to Faster R-CNN is talked about and efficiency of region proposal and representational richness are improved. The single-shot detectors that are investigated are YOLO, SSD, and RetinaNet as they can offer high-speed inference, thus they are applicable to the real-time or mobile photography case. The study also examines photography-focused datasets like COCO, Open Images and expert-curated collections, which are annotation formats and augmentation strategies, which are taken into account in artistic variability, lighting and composition issues common to both professional and amateur photography. A new architecture based on applying modern backbones: ResNet, EfficientNet, and Swin Transformer and flexible detection heads is proposed. The loss functions that encompass robust localization, classification refinement, and variants of the IoU are combined so that they optimize the performance in various photographic scenes. Applications have shown very strong effect: automated tagging and image organization, real-time detection of both DSLR/mobile systems, and intelligent aid to the creation of art and subject-awareness to enhance composition

Amateur · Computational photography · Deep learning · Detector · Object detection · Photography · Transformer · Advanced Image and Video Retrieval Techniques · Aesthetic Perception and Analysis · Visual Attention and Saliency Detection · Architecture