---
title: 'CLCXray: X-Ray Illicit Object Detection Benchmark'
url: https://www.emergentmind.com/topics/clcxray
type: topic
---

# CLCXray: X-Ray Illicit Object Detection Benchmark

CLCXray is a public benchmark for illicit object detection in X-ray security imagery. In comparative evaluation literature, it is described as an object detection dataset with **12 classes**, **9,565 images**, **bounding-box annotations**, **RGB pseudo-colored imagery**, and **dual-energy X-ray acquisition**, and it is used to study detection under frequent overlap among objects, including liquid-container categories that are stated to be absent from other datasets [2507.17508]. A separate study on edge-oriented threat detection likewise treats CLCXray as an open-source X-ray security baggage dataset from the real subway security baggage screening process, converted into YOLO format for 12-class object detection [2309.15601].

## 1. Definition and benchmark role

CLCXray is identified in the benchmarking literature as the dataset introduced by Zhao et al. for **“Detecting Overlapped Objects in X-Ray Security Imagery by a Label-Aware Mechanism.”** It is positioned as a benchmark designed to address two limitations of earlier X-ray security datasets: insufficient overlap among foreground objects and neglect of liquid containers. In the six-dataset comparison comprising OPIXray, CLCXray, SIXray, EDS, HiXray, and PIDray, CLCXray is treated as the benchmark most directly associated with **object overlap/occlusion with same-class instances and background clutter**, rather than with scanner-domain shift or tiny deliberately hidden objects [2507.17508].

This role is technically consequential. The comparative study characterizes CLCXray as a regime in which detectors must cope with **overlapped, often large, foreground items**, including both cutters and liquid containers. Relative to OPIXray, it is described as less about explicit occlusion-level stratification and more about frequent overlaps among multiple foreground objects; relative to PIDray, it occupies the opposite size regime, because PIDray contains only small objects whereas CLCXray is overwhelmingly large-object; relative to EDS, it does not target domain shift across scanners [2507.17508].

## 2. Dataset composition, classes, and split structure

CLCXray combines **real subway security inspection imagery** with **simulated baggage setups**. Of the **9,565 total images**, **4,543 are real samples** collected from **subway scan inspection systems**, and **5,022 are simulated** by scanning artificially designed baggage arrangements. The dataset is split into **training (80%)**, **validation (10%)**, and **test (10%)** subsets; one implementation-oriented study gives the corresponding counts as **7,652 training images**, **955 validation images**, and **955 test images** [2507.17508; 2309.15601].

| Category | Classes |
|---|---|
| Cutter | Blade (BL), Dagger (DA), Knife (KN), Scissors (SC), Swiss army knife (SW) |
| Liquid-container | Can (CA), Carton drink (CD), Glass bottle (GB), Plastic bottle (PL), Vacuum cup (VA), Spray can (SP), Tin (TI) |

A dataset-specific and methodologically important detail is that the **test set uses a 1:9 real-to-simulated ratio**, whereas the **training and validation sets use an 8:1 real-to-simulated ratio** [2507.17508]. This suggests an internal train–test composition shift rather than a purely i.i.d. split. One paper additionally states that the data were **pre-processed and converted into YOLO format**, but does not provide a more detailed annotation schema, class remapping policy, or relabeling procedure [2309.15601].

## 3. Overlap structure, object scale, and class difficulty

The dataset is explicitly described as overlap-heavy. On average, **each image contains more than two potentially dangerous items**, and **nearly 60% of images contain at least two or more foreground objects**. In the evaluated test partition used in the comparative study, CLCXray contains **1,421 total test objects**, of which **3 are small**, **225 medium**, and **1,193 large** [2507.17508].

These statistics shape the interpretation of detector behavior. The comparative analysis argues that CLCXray favors architectures that can recognize **large objects** under overlap-rich conditions. The same study reports that **Knife (KN)** is among the easier object types, whereas **Glass bottle (GB)** is among the harder ones. The broader explanation given is based on **material attenuation** and **signature complexity**: high-density metallic objects generally produce stronger contrast in X-ray imaging, while low-density or less distinctive liquid-container objects remain difficult [2507.17508].

Within the material-complexity discussion, **Carton drink (CD)** and **Glass bottle (GB)** are listed among low-attenuation low-complexity signatures, while **Vacuum cup (VA)** and **Spray can (SP)** fall into higher-complexity or pressurized-container categories [2507.17508]. This suggests that CLCXray is not merely an overlap benchmark; it is also a benchmark in which class difficulty varies with X-ray attenuation properties and container morphology.

## 4. Evaluation protocol and comparative detector behavior

The unified comparative study evaluates CLCXray with **mAP\(^ {50}\)** and **mAP\(^ {50:95}\)** for detection quality, and **inference time (ms)**, **parameter size (M)**, and **GFLOPS** for efficiency and computational complexity. The definitions are given as follows:  
\[
\text{IoU} = \frac{|A \cap B|}{|A \cup B|} \in [0,1],
\]
\[
\text{AP}=\int_0^1 p(r)\,dr \in [0,1],
\]
\[
\text{mAP}=\frac{1}{N}\sum_n^N \text{AP}_n,
\]
where \(N\) is the number of classes. The study further specifies that **mAP\(^ {50}\)** uses IoU threshold **0.5**, while **mAP\(^ {50:95}\)** averages AP across IoU thresholds from **0.5 to 0.95 in steps of 0.05** [2507.17508].

For CLCXray, **YOLOv8 and RT-DETR** are trained using **SGD**, with **learning rate 0.01** and **momentum 0.9**; the custom CNN variants **CHR, DOAM, and LIM** use the same optimizer and hyperparameters. **DINO** uses **AdamW**, **learning rate 0.0001**, **\(\beta_1=0.9\)**, and **weight decay 0.0001**; **Co-DETR** uses **AdamW**, **learning rate 0.0002**, **\(\beta_1=0.9\)**, and **weight decay 0.0001**. **YOLOv8, RT-DETR, CHR, DOAM, and LIM** are trained for **100 epochs**, while **DINO and Co-DETR** are trained for **36 epochs**; **early stopping** is used throughout [2507.17508].

| Detector | mAP\(^ {50}\) | mAP\(^ {50:95}\) |
|---|---:|---:|
| D(Co-DETR, Swin-B) | 0.772 | 0.654 |
| D(YOLOv8, Next-ViT-S) | 0.740 | 0.640 |
| D(YOLOv8, CSPDarkNet53) | 0.733 | 0.636 |

Across all ten detectors, the **best overall detector under both metrics is D(Co-DETR, Swin-B)**, with **mAP\(^ {50}=0.772\)** and **mAP\(^ {50:95}=0.654\)**. The comparative study interprets this as evidence that **transformer and hybrid detectors with transformer backbones outperform CNNs on CLCXray**, “mainly due to the efficiency of transformer blocks in recognizing large objects.” The same analysis also reports a negative result: the custom X-ray-specific CNN modules **CHR**, **DOAM**, and **LIM** all **underperform the generic YOLOv8 baseline** on CLCXray, despite the dataset’s original emphasis on overlap handling [2507.17508].

The efficiency trade-off is explicit. The most accurate model, **D(Co-DETR, Swin-B)**, is also the slowest and heaviest among the leading contenders, at **187 ms inference time**, **125M parameters**, and **1068 GFLOPS**. By contrast, **D(YOLOv8, CSPDarkNet53)** reaches **0.636 mAP\(^ {50:95}\)** with **7.52 ms**, **43.6M parameters**, and **165.4 GFLOPS**, while **D(YOLOv8, Next-ViT-S)** reaches **0.640 mAP\(^ {50:95}\)** with **12.22 ms**, **56M parameters**, and **174.9 GFLOPS** [2507.17508].

## 5. Resource-constrained detection and QCFS Tiny YOLOv7

A distinct line of work uses CLCXray as the target benchmark for **resource-constrained X-ray baggage threat object detection**. That study evaluates three related detector families: a conventional **Tiny YOLOv7 ANN**, a **QCFS Tiny YOLOv7**, and a partially converted **SNN Tiny YOLO** [2309.15601].

The architectural change in the QCFS model is narrowly defined. The authors modify Tiny YOLOv7 by **replacing the standard activation in BConv with QCFS** and **replacing MaxPool with AvgPool**. The rationale is ANN-to-SNN conversion theory: the paper states that when the shift hyperparameter \(\varphi=\frac12\), the expected conversion error is zero,
\[
\forall\:T, L \;\;\;\;\;\;\; E_z(\hat{e_l})|_{\varphi=\frac{1}{2} = 0.
\]
The best QCFS result reported on CLCXray is obtained at **4 quantization steps** [2309.15601].

After **50 epochs**, the paper reports approximate validation-style results of **mAP@.5 ≈ 0.813**, **mAP@.5:.95 ≈ 0.628**, and **F1 = 0.80** for Tiny YOLOv7, versus **mAP@.5 = 0.836**, **mAP@.5:.95 ≈ 0.649**, and **F1 = 0.82** for QCFS Tiny YOLOv7. On the held-out test set, the reported values are **0.616 mAP@.5** and **0.60 F1** for Tiny YOLOv7, versus **0.633 mAP@.5** and **0.62 F1** for QCFS Tiny YOLOv7 [2309.15601].

The same study also evaluates partial spiking conversion. When the **first QCFS activation** is converted to a neuron, performance drops to **mAP@.5 < 0.01**; when the **last QCFS activation** is converted, it reaches **0.375 mAP@.5** [2309.15601]. This indicates that, within that study, the practical deployment winner is the **QCFS ANN**, not the converted spiking model. The paper claims that QCFS Tiny YOLO achieves **state-of-the-art performance on CLCXray**, but it does **not** provide a formal comparison table against prior published CLCXray methods, so the strongest directly demonstrated conclusion is narrower: **QCFS outperforms the authors’ own standard Tiny YOLOv7 baseline on CLCXray** [2309.15601].

## 6. Reproducibility, limitations, and interpretive issues

CLCXray is comparatively well specified at the benchmark-summary level and less well specified at the low-level data-pipeline level. In the large comparative study, the framework code and model weights are made publicly available, and the implementation is said to rely on the authors’ released code together with **Ultralytics** and **MMDetection**; backbone initialization is reported as **COCO-pretrained** for **CSPDarkNet53** and **HGNetV2**, **ImageNet-22k-pretrained** for **Swin-B**, and **ImageNet-1k-pretrained** for **Next-ViT-S** [2507.17508]. In the Tiny YOLO study, the transfer-learning starting point is **MS COCO pretrained Tiny YOLOv7 weights** [2309.15601].

At the same time, several reproducibility gaps are explicit. The comparative study does **not** provide CLCXray-specific resizing, normalization constants, augmentation recipes, label cleaning, annotation correction, relabeling, batch size, input resolution, scheduler, warmup policy, or mixed-precision details; the Tiny YOLO study likewise omits image resolution statistics, resizing policy, normalization specifics, augmentation details, anchor recalculation, class balancing strategy, and hardware-level deployment measurements such as latency, FPS, memory usage, power, or energy [2507.17508; 2309.15601].

A recurrent interpretive issue is leaderboard comparability. The benchmark-scale study evaluates ten detectors under a common protocol and emphasizes that detector choice in X-ray screening should be **dataset- and challenge-aware rather than architecture-dogmatic** [2507.17508]. By contrast, the resource-constrained study reports strong QCFS results but does not present a formal cross-paper comparison table, so its “state-of-the-art” claim is not fully substantiated within the paper itself [2309.15601]. This suggests that CLCXray should be treated less as a single settled ranking and more as a benchmark whose conclusions depend on protocol, metric, and deployment constraint.

In the arXiv literature considered here, CLCXray is therefore best understood as a specialized benchmark for **overlap-heavy, mostly large-object X-ray threat detection**. Its distinctive properties are the coexistence of **same-class/background overlap**, **multiple foreground objects per image**, **predominantly large instances**, and **liquid-container categories**. Those properties explain why attention-based models can perform strongly on it, why low-attenuation liquid objects remain challenging, and why it remains useful as a complement to other public X-ray benchmarks rather than a substitute for them [2507.17508].

Source: https://www.emergentmind.com/topics/clcxray