OCDet: Object Center Detection via Bounding Box-Aware Heatmap Prediction on Edge Devices with NPUs

Real-time object localization on edge devices is fundamental for numerous applications, ranging from surveillance to industrial automation. Traditional frameworks, such as object detection, segmentation, and keypoint detection, struggle in resource-constrained environments, often resulting in substantial target omissions. To address these challenges, we introduce OCDet, a lightweight Object Center Detection framework optimized for edge devices with NPUs. OCDet predicts heatmaps representing object center probabilities and extracts center points through peak identification. Unlike prior methods using fixed Gaussian distribution, we introduce Generalized Centerness (GC) to generate ground truth heatmaps from bounding box annotations, providing finer spatial details without additional manual labeling. Built on NPU-friendly Semantic FPN with MobileNetV4 backbones, OCDet models are trained by our Balanced Continuous Focal Loss (BCFL), which alleviates data imbalance and focuses training on hard negative examples for probability regression tasks. Leveraging the novel Center Alignment Score (CAS) with Hungarian matching, we demonstrate that OCDet consistently outperforms YOLO11 in object center detection, achieving up to 23% higher CAS while requiring 42% fewer parameters, 34% less computation, and 64% lower NPU latency. When compared to keypoint detection frameworks, OCDet achieves substantial CAS improvements up to 186% using identical models. By integrating GC, BCFL, and CAS, OCDet establishes a new paradigm for efficient and robust object center detection on edge devices with NPUs. The code is released at https://github.com/chen-xin-94/ocdet.

DART: An Automated End-to-End Object Detection Pipeline with Data Diversification, Open-Vocabulary Bounding Box Annotation, Pseudo-Label Review, and Model Training

Swift and accurate detection of specified objects is crucial for many industrial applications, such as safety monitoring on construction sites. However, traditional approaches rely heavily on arduous manual annotation and data collection, which struggle to adapt to ever-changing environments and novel target objects. To address these limitations, this paper presents DART, an automated end-to-end pipeline designed to streamline the entire workflow of an object detection application from data collection to model deployment. DART eliminates the need for human labeling and extensive data collection while excelling in diverse scenarios. It employs a subject-driven image generation module (DreamBooth with SDXL) for data diversification, followed by an annotation stage where open-vocabulary object detection (Grounding DINO) generates bounding box annotations for both generated and original images. These pseudo-labels are then reviewed by a large multimodal model (GPT-4o) to guarantee credibility before serving as ground truth to train real-time object detectors (YOLO). We apply DART to a self-collected dataset of construction machines named Liebherr Product, which contains over 15K high-quality images across 23 categories. The current implementation of DART significantly increases average precision (AP) from 0.064 to 0.832. Furthermore, we adopt a modular design for DART to ensure easy exchangeability and extensibility. This allows for a smooth transition to more advanced algorithms in the future, seamless integration of new object categories without manual labeling, and adaptability to customized environments without extra data collection. The code and dataset are released at https://github.com/chen-xin-94/DART.