As one of the core applications of computer vision, object detection has become more important in scenarios requiring high accuracy but with limited computational resources such as robotics and autonomous vehicles. Object detection using machine learning running on embedded device such as Raspberry Pi provides the high possibility to detect any custom objects without the recalibration of camera. In this work, we developed a smart and lean object detection model for shipping containers by using the state-of-the-art deep learning TensorFlow model and deployed it to a Raspberry Pi. Using EfficientDet-Lite2, we explored the different cross-validation strategies (Hold-out and K-Fold). The experimental results show that compared with the baseline EfficientDet-Lite2 algorithm, our model improved the mean average precision (mAP) by 44.73% for the Hold-out dataset and 6.26% for K-Fold cross-validation. We achieved Average Precision (AP) of more than 80% and best detection scores of more than 93% for the Hold-out dataset. For the 5-Fold lean dataset, the results show the Average Precision across the three lightweight models are generally high as the models achieved more than 50% average precision, with YOLOv4 Tiny performing better than EfficientDet-Lite2 and Single Shot Detector (SSD) MobileNet V2 Feature Pyramid Network (FPN) 320 as a lightweight model.

Object detection for a pick-and-place system has been widely acknowledged as a significant research area in the field of computer vision. The integration of AI and machine vision with pick-and-place operations should be made affordable for Small and Medium Enterprises (SMEs) so they can leverage this technology. Therefore, the aim of this study is to develop a smart and lean pick-and-place solution for custom workpieces, which requires minimal computational resources. In this study, we evaluate the effectiveness of illumination and batch size to improve the Average Precision (AP) and detection score of an EfficientDet-Lite model. The addition of 8% optimized bright Alpha3 images results in an increase of 7.5% in AP and a 6.3% increase in F1-score as compared to the control dataset. Using a training batch size of 4, the AP is significantly improved to 66.8% as compared to a batch size of 16 at 57.4%. The detection scores are improved to 80% with a low variance of 1.65 using a uniform 135-angle lamp and 0 illumination level. The pick-and-place solution is validated using Single-Shot Detector (SSD) MobileNet V2 Feature Pyramid Network (FPN) Lite. Our experimental results clearly show that the proposed method has an increase of 5.19% in AP compared to SSD MobileNet V2 FPNLite.

Pick-and-place operations are an integral part of robotic automation and smart manufacturing. By utilizing deep learning techniques on resource-constraint embedded devices, the pick-and-place operations can be made more accurate, efficient, and sustainable, compared to the high-powered computer solution. In this study, we propose a new technique for object detection on an embedded system using SSD Mobilenet V2 FPN Lite with the optimisation of the hyperparameter and image enhancement. By increasing the Red Green Blue (RGB) saturation level of the images, we gain a 7% increase in mean Average Precision (mAP) when compared to the control group and a 20% increase in mAP when compared to the COCO 2017 validation dataset. Using a Learning Rate of 0.08 with an Edge Tensor Processing Unit (TPU), we obtain high real-time detection scores of 97%. The high detection scores are important to the control algorithm, which uses the bounding box to send a signal to the collaborative robot for pick-and-place operation.

This study presents a zero-shot object detection framework for corner casting detection in shipping container operations, leveraging edge computing for intelligent robotic perception and control. The proposed system integrates Grounding DINO on a Raspberry Pi, utilizing Referring Expression Comprehension (REC) and Additional Feature Keywords (AFKs) to enable precise corner casting localization without model retraining. This approach reduces computational overhead while ensuring real-time deployment suitability for robotics applications. A comparative evaluation against three SSD-based models—SSD320 MobileNet-V2 FPNLite, MobileNet-V2, and EfficientDet-Lite0—reveals that Grounding DINO achieves a 7.14% higher detection score. Furthermore, a statistical effect size analysis using Cohen’s d (d = 2.2) confirms a significant performance advantage, reinforcing Grounding DINO’s efficacy in zero-shot scenarios. These findings underscore the potential of LLM-driven object detection in resource-constrained environments, offering a scalable and adaptable solution for intelligent perception and control in robotics.

Automated port operations rely on accurate detection of container corner casting holes, which are essential for crane alignment and twist-lock engagement. Real-world challenges such as dim lighting, occlusion and rusting make this a safety-critical task that traditional computer vision framework struggle to address. To overcome these constraints, this study employs zero-shot detection (ZSD) with Grounding DINO, a state-of-the-art vision–language model that aligns natural language prompts with visual regions. While Grounding DINO provides strong baseline performance, its outputs are highly sensitive to prompt phrasing and contextual detail. This work introduces Prompt Complexity Score (PCS), a quantitative and interpretable framework for analysing the impact of prompt formulation on zero-shot detection performance in vision–language models. Unlike existing prompt-tuning approaches that rely on model adaptation, PCS provides a model-agnostic metric that links linguistic structure to detection outcomes, enabling systematic evaluation and optimisation of prompts without retraining. The robustness of the framework was validated through regression analysis, showing that the normalised form, PCS_normalised = PCS/log₂(TL+1), is the strongest predictor of detection accuracy, with cubic regression achieving R2 = 0.803, p < 0.001, and a 95 % confidence interval [CI] of 0.62 to 0.91. By integrating PCS with ZSD using Grounding DINO, this work transforms prompt engineering from ad hoc practice into a statistically validated methodology, strengthening safety in port automation and offering a scalable framework for industrial deployment.