Development of an Image Data Set of Construction Machines for Deep Learning Object Detection
Bibliographic record
Abstract
Deep learning object detection algorithms have proven their capacity to identify a variety of objects from images and videos in near real-time speed. The construction industry can potentially benefit from this machine intelligence by linking algorithms with construction videos to automatically analyze productivity and monitor activities from a safety perspective. However, an effective image data set of construction machines for training deep learning object detection algorithms is not currently available due to the limited accessibility of construction images, the time-and-labor-intensiveness of manual annotations, and the knowledge base required in terms of both construction and deep learning. This research presents a case study on developing an image data set specifically for construction machines named the Alberta Construction Image Data Set (ACID). In the case of ACID, 10,000 images belonging to 10 types of construction machines are manually collected and annotated with machine types and their corresponding positions on the images. To validate the feasibility of ACID, we train the data set using four existing deep learning object detection algorithms, including YOLO-v3, Inception-SSD, R-FCN-ResNet101, and Faster-RCNN-ResNet101. The mean average precision (mAP) is 83.0% for Inception-SSD, 87.8% for YOLO-v3, 88.8% for R-FCN-ResNet101, and 89.2% for Faster-RCNN-ResNet101. The average detection speed of the four algorithms is 16.7 frames per second (fps), which satisfies the needs of most studies in the field of automation in construction.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".